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The integration of distributed energy resources (DERs) such as wind, solar, batteries, etc. into the grid presents opportunities as well as challenges with regard to efficient coordination and trading. Virtual power plants (VPPs) have emerged as an important paradigm for aggregating and managing numerous DERs. This paper proposes a decentralized blockchain-based peer-to-peer (P2P) multilayer energy trading framework to facilitate optimized coordination of DERs within a VPP context. A control center, prosumers, and service operators constitute the model's multi-layered architecture. Profits from feed-in tariffs, ancillary services, and P2P sales are optimized for revenue while expenses are minimized using a mixed-integer linear programming formulation. Smart contracts enable prosumers to trade energy units in the Ethereum blockchain with ease via a participatory web platform. The effectiveness of the framework in synchronizing power supply and demand within the VPP by coordinating the charging and discharging of storage systems in accordance with consumption patterns is demonstrated through performance evaluation. In contrast to conventional centralized architectures, the proposed framework utilized decentralized architecture efficiently consolidates and optimizes distributed energy resources (DERs) to improve grid stability and renewable energy utilization. [Display omitted] • A novel multi-layer framework for peer-to-peer (P2P) energy trading, within VPP. • An interactive web-based trading platform using the Ethereum blockchain to facilitate exchange of energy units. • Implementation of MILP-based DER management model for optimal trading. [ABSTRACT FROM AUTHOR]

Forecasting energy demand has become increasingly important due to technological advances, especially new power systems and population growth. Accurate predictions of energy demand and supply are crucial for academics and policymakers. Also, energy consumption is non-stationary and dynamic in time, requiring an adaptive forecasting algorithm. This paper presents a new adaptive hybrid approach for energy time series forecasting using statistical methods. Some of the most popular energy demand time series applications are used to demonstrate the superiority of the proposed algorithm, which covers a wide range of critical energy demand forecasting situations at various scales. The paper's objectives are presented in three phases. In Phase I, ARFIMA is used to forecast the entire original time series as a statistical inspector and later for comparison. Although ARFIMA's fractionality feature helps to capture long and short-term memory behavior in time series, to be more adaptive and become close to a nonlinear algorithm that can handle non-stationarity, and Gaussian and non-Gaussian time series closely, a novel dynamic statistical structural break detection framework is developed, and used in phase II to identify the time series' change points and associated time indexes. Since time series are partitioned based on structural change detections, they approach one in terms of the Hurst exponent and exhibit pure long memory, which is ideal for ARFIMA modeling. Phase III captures non-stationary statistical properties and memory characteristics by applying adaptive ARFIMA modeling to segmented time series. Finally, the predicted partitions are concatenated. Various performance assessment metrics, e.g., Mean Absolute Percentage Error (MAPE), tested on the final achieved results show that the proposed adaptive algorithm outperforms the forecasting capabilities of the existing algorithms on the same energy demand time series used in the current paper. MAPE of the proposed approach for the four utilized real-life cases are 1.72%, 1.35%, 0.22%, and 4.4%, respectively. Therefore, the proposed model is a satisfactory method for energy demand forecasting due to its high accuracy. • A novel adaptive hybrid algorithm is developed for energy demand forecasting. • The prediction system uses ARFIMA and a novel dynamic structural break detection framework. • The adaptive prediction method can forecast energy demand with high accuracy. • The structural break detection works dynamically, allowing ARFIMA to be adaptive. • The prediction method captures the energy time series' nonstationary statistical features. [ABSTRACT FROM AUTHOR]

In the realm of economic scheduling and optimal operation within integrated energy systems, integrated energy load aggregation plays a pivotal role. Accurate prediction of integrated energy load aggregation enables the refined management of energy resources, thereby enhancing energy utilization efficiency. This paper merges the advantages of clustering algorithms, prediction algorithms and ensemble learning to propose a short-term prediction approach for integrated energy load aggregation. Firstly, the intrinsic distribution of energy consumption data is analyzed through Affinity Propagation (AP) clustering with multi-dimensional similarity. This analysis yields the division of all samples into clusters based on data characteristics. Subsequently, the Transfer entropy is harnessed to conduct correlation analysis within each cluster. For constructing predictive models for each individual cluster, a prediction algorithm based on the Feature-Temporal Attention-enhanced Bi-directional Simple Recurrent Unit (FTA-Bi-SRU) is employed. The Dynamic Weight Average (DWA)-based multi-task loss equilibrium and Multi-Term Adam (MTAdam) loss function are combined to enhance the efficiency of the forecasting model. Finally, a Light Gradient Boosting Machine (LightGBM) model with Bayesian optimization is employed to integrate the predictive outcomes of each individual cluster to generate the overall predictive outcomes for integrated energy load aggregation. The proposed methodology is experimented on real-world energy consumption data to ascertain the efficacy of the prediction method. The experimental results substantiate the superiority of the suggested forecasting approach. • This marks the inaugural discussion of load forecasting for integrated energy load aggregation is discussed. • A multi-dimensional similarity Affinity Propagation (AP) clustering algorithm is introduced to group multi-energy users. • A method based on transfer entropy for correlation analysis is presented. • An Feature-Temporal Attention based Bi-directional Simple Recurrent Unit (FTA-Bi-SRU) prediction model is introduced. • A Bayes-optimized Light Gradient Boosting Machine (BO-LightGBM) ensemble framework is proposed to enhance overall prediction. [ABSTRACT FROM AUTHOR]

This paper investigates the cyber resiliency of future power systems with high penetration of distributed energy resources using advanced distributed and (or) hierarchical control architectures. Specifically, we simulate cyberattacks on three prototypical use cases, and we identify attack scenarios that are the most damaging to the overall system performance. We show that these attacks can have a significant impact on grid operation. Results provide additional insights into the robustness of the system to the most common cyberattacks. • This paper delves into the realm of cyber-resiliency, examining its implications across three distinct use cases encompassing distributed, distributed-hierarchical, and hierarchical control architectures. • Through exhaustive numerical simulations, we discern and highlight the precise signals within the system that exhibit heightened susceptibility to cybersecurity attacks. • The results obtained from our analysis offer valuable insights into the robustness of these systems, shedding light on their ability to withstand potential cybersecurity threats. [ABSTRACT FROM AUTHOR]

Highlights • Review of the application of reinforcement learning (RL) for demand response (DR) • DR is relevant for integrating renewable energy source into the smart grid. • Considering RL/DR from energy generation and storage, to supply and user satisfaction. • Typical algorithms and open research directions are discussed. • Most articles focus on single-agent systems in stationary environments. Abstract Buildings account for about 40% of the global energy consumption. Renewable energy resources are one possibility to mitigate the dependence of residential buildings on the electrical grid. However, their integration into the existing grid infrastructure must be done carefully to avoid instability, and guarantee availability and security of supply. Demand response, or demand-side management, improves grid stability by increasing demand flexibility, and shifts peak demand towards periods of peak renewable energy generation by providing consumers with economic incentives. This paper reviews the use of reinforcement learning, a machine learning algorithm, for demand response applications in the smart grid. Reinforcement learning has been utilized to control diverse energy systems such as electric vehicles, heating ventilation and air conditioning (HVAC) systems, smart appliances, or batteries. The future of demand response greatly depends on its ability to prevent consumer discomfort and integrate human feedback into the control loop. Reinforcement learning is a potentially model-free algorithm that can adapt to its environment, as well as to human preferences by directly integrating user feedback into its control logic. Our review shows that, although many papers consider human comfort and satisfaction, most of them focus on single-agent systems with demand-independent electricity prices and a stationary environment. However, when electricity prices are modelled as demand-dependent variables, there is a risk of shifting the peak demand rather than shaving it. We identify a need to further explore reinforcement learning to coordinate multi-agent systems that can participate in demand response programs under demand-dependent electricity prices. Finally, we discuss directions for future research, e.g., quantifying how RL could adapt to changing urban conditions such as building refurbishment and urban or population growth. [ABSTRACT FROM AUTHOR]

This paper offers a comprehensive overview of advanced water desalination systems powered by renewable energy sources, specifically focusing on developments in the past decade. Solar energy is particularly emphasized as a pivotal resource for powering diverse desalination methods, including membrane distillation, reverse osmosis, humidification-dehumidification, forward osmosis, and hybrid systems. The primary objectives of this study encompass evaluating current advancements in renewable energy systems, especially solar energy, for driving water desalination processes. Furthermore, the study aims to identify gaps in research, challenges, and recommendations within these processes, and to assess the performance and refinement potential of hybrid desalination systems. Through a comprehensive analysis of various investigations, integrated effectiveness and cost-efficiency are examined, focusing on practical energy utilization. Notably, the analysis reveals the potential of hybrid desalination systems to reduce energy consumption. For instance, the hybridization of forward osmosis with membrane distillation showcases a significant energy ratio decrease from 0.89 to 0.64. Moreover, the combination of humidification-dehumidification with reverse osmosis is shown to reduce energy usage while enhancing freshwater output by up to 38% compared to standalone reverse osmosis. However, challenges persist, such as the need for improved integration of hybrid systems with concentrated solar collectors like photovoltaic thermal and concentrated photovoltaic thermal technologies. • A comprehensive review of water desalination systems powered by renewable energy. • The importance of combination of solar energy with hybrid desalination systems. • Renewable energy integration with different desalination techniques. • A comparative analysis of integration of renewable energy with hybrid desalination. • Challenges and future recommendations for successful integration of renewable energy with desalination. [ABSTRACT FROM AUTHOR]

Wave energy resources have high, yet largely untapped potential as candidate generation technology. In this paper, we perform a data-driven analysis to characterize the impact of wave energy integration on bulk-scale power systems and market operations. Through data-driven sensitivity studies centered on an optimization-based production cost modeling formulation, our work characterizes the inflection point beyond which wave integration starts impacting power system operations, considering present day transmission infrastructure. Furthermore, our analysis also considers the joint effects of wave energy integration and system-wide transmission expansion. Finally, potential resilience scenarios such as wildfire-driven transmission contingencies and heat wave events are investigated, whereby the contributions of grid-integrated wave energy in alleviating the effects of the resilience events are analyzed. As our demonstration test bed, we consider a reduced-order network topology for the U.S. Western Interconnection with wave energy generation integrated at carefully selected sites across the coastal areas of Washington, Oregon, and northern California. Our results indicate that over a representative year of operations, wave energy integration systematically reduces locational marginal prices (LMPs) of energy and price volatility, especially during periods of high wave resource availability (winter months for the U.S. west coast). Average, maximum, and minimum of hourly LMPs over a typical year of operation was reduced by 2.95, 51.28, and 1.13 $/MWh respectively (over a baseline scenario with no wave energy integration), when the selected network model had a total of 5000 MW wave power installed capacity during the representative year of study. The effects of wave energy integration can remain localized with existing transmission infrastructure (identified to be most pronounced in the Pacific Northwest region in the example we studied). However, with concurrent transmission expansion, the impacts of wave energy integration are likely to have a higher geographical spread. Our results also indicate that wave energy may be able to assist power system operations during resilience events such as major transmission contingencies and heat wave events, although such benefits might be dependent on factors such as proximity of affected area to wave resources, availability of adequate resource potential and adequate transmission capacity. • Production cost model of a bulk power system with wave power is developed. • Sensitivity studies to infer impact of wave power on grid operations at varying capacities. • Wave power reduces electricity prices during periods of higher resource availability. • Wave power benefits can percolate inland with system-wide transmission expansion. • Wave power can have added benefits in resilience events like transmission outages. [ABSTRACT FROM AUTHOR]

In the 21st century, urbanization has caused rapid growth in the global construction area, resulting in high building resource consumption and long-term environmental impacts. Analyzing the resource consumption of public buildings and identifying the key factors that affect the consumption of building resources is important for developing of targeted policies. In this paper, we analyzed each branch of the People's Bank of China based on energy, water, and land resource consumption as well as the structure of energy consumption. Based on resource-consumption indicators, we divided 31 provincial-level administrative regions into four resource consumption levels; 65% were in the low-to-medium resource consumption levels. Medium-sized buildings had the highest energy consumption level and a more balanced energy consumption structure. The energy consumption per capita, water consumption, and utilization rate of small-sized buildings were low. To reduce resource consumption by public buildings, the construction of large-sized buildings should be limited. • The comprehensive evaluation method is used to calculate the resource consumption of the People's Bank of China. • Regression equations were established for energy consumption and resource consumption. • Medium-sized institutions had the highest energy consumption level and a more balanced energy consumption structure. [ABSTRACT FROM AUTHOR]

The increasing effects of global warming and energy depletion have raised concerns about the pollution caused by traditional oil and fossil energy usage. Distributed energy resources (DERs) have emerged as promising techniques to address these issues. However, the growing proportion of power injection from DERs presents technical challenges such as power quality decline and power flow reversal, which may destabilize the grid. Non-intrusive load monitoring (NILM) is a promising and cost-effective approach that can provide residential power information to improve grid scheduling, dispatching, and optimize residential power consumption behavior. This paper proposes a novel NILM method that can decompose the power of both household appliances and DERs integrated with residential houses. The proposed approach employs a data segment method that utilizes adaptive window lengths to identify the behavior of DERs based on the operating periods and characteristics of different equipment. A non-intrusive load monitoring method is proposed based on the ResNet-seq2seq framework to address the problem of model degradation and gradient vanishing. Post-processing techniques are applied to the output results using a feature list of the target equipment. Experiments are conducted on a dataset organized based on the REDD and Pecan Street datasets to demonstrate the accuracy and effectiveness of the proposed method. The results show that compared to the start-of-the-art CNN-seq2seq model, the proposed method achieved 54.95%, 69.18%, 56.95%, and 84.98% reduction in MAE for PV, EV, refrigerators, and microwaves, respectively. • A novel ResNet-seq2seq-based NILM approach is proposed for load disaggregation of residential houses integrating DERs. • Wavelet denoising and adaptive window length data segments are used for data pre-processing and accurate disaggregation. • Residual connections are employed to solve model degradation and vanishing gradient issues as network depth increases. • A post-processing technique is applied to modify the output of the network and improve disaggregation accuracy. [ABSTRACT FROM AUTHOR]

With the progress of decarbonization, renewable-powered microgrids are attracting wide attention. To cope with the fluctuation of renewable power at different timescales, both long-term and short-term energy storage devices are required. This paper studies the operation of renewable-dominated isolated microgrids integrated with hybrid seasonal-battery storage. A data-driven scheduling-correction framework is proposed. By leveraging the historical data of renewable power and load, the scheduling module generates ex-post optimal state-of-charge (SoC) sequences of the seasonal energy storage ahead of the operating year. In each period of real-time operation, the correction module performs two steps: the first step is to update the reference SoC for the seasonal storage based on the ex-post optimal SoC sequences and the newly observed data; the second step is to solve a bi-objective rolling-horizon optimization problem which minimizes the instant operating cost while steering the SoC of seasonal storage to its reference value. An appealing feature of the proposed method is that the long-term renewable power forecasts are not required. A microgrid system is devised to verify the proposed framework. Numerical tests show that the scheduling-correction framework outperforms existing rolling horizon approaches in compromising economy, power supply reliability, and renewable energy utilization. • A data-driven scheduling-correction framework is proposed to provide real-time actions with a global vision, making better use of the seasonal hydrogen storage. In the scheduling module, ex-post optimal SoC sequences of the seasonal storage are generated using historical data. In the correction module, the real-time operation relies on a bi-objective rolling-horizon optimization problem which minimizes the operating cost while following the SoC reference of the seasonal storage, which is sequentially updated along with time. • The kernel regression technique is established to sequentially update the SoC reference of the seasonal hydrogen storage. This technique calibrates a conditional probability distribution based on the newly observed data; the resulting distribution (weight coefficients) interprets how likely the current year is similar to each of the past years. The reference is updated in each period according to the weighted sum of the ex-post optimal SoC sequences. • Systematic tests are conducted to compare the cost, load shedding, and renewable energy curtailment achieved by different methods. Numerical results show that the scheduling-correction framework can fully exploit the seasonal storage. The proposed method outperforms existing rolling horizon approaches in compromising economy, power supply reliability, and renewable energy utilization. [ABSTRACT FROM AUTHOR]

In Industry 4.0, the production data obtained from the Internet of Things has reached the magnitude of big data with the emergence of advanced information and communication technologies. The massive and low-value density of big data challenges traditional clustering and correlation analysis. To solve this problem, a big data-driven correlation analysis based on clustering is proposed to improve energy and resource utilisation efficiency in this paper. In detail, the production units with abnormal and energy-intensive consumption can be classified by using clustering analysis. Additionally, feature extraction is carried out based on clustering analysis and the same cluster data is migrated to the training data set to improve correlation analysis accuracy. Then, correlation analysis can balance the relationship between energy supply and demand, which can reduce carbon emission and enhance sustainable competitiveness. The sensitivity analysis results show that the feature extraction method can improve the correlation analysis accuracy compared to the original analysis model. In conclusion, the big data-driven correlation analysis based on clustering can uncover the potential relationship between energy consumption and product yield, thus improving the efficiency of energy and resources. • An architecture of big data clustering and correlation analysis is proposed. • A PCA-guided ISODATA is proposed for data pre-processing and processing. • The feature extraction method has been proposed based on clustering. • A correlation analysis model is established for data mining. • The models can improve the scheduling efficiency of energy and resources. [ABSTRACT FROM AUTHOR]

Some scholars have addressed the relationship between the Human Development Index (HDI) and energy consumption in terms of the semi-logarithmic or hyperbolic function. One of the results from this relationship is to divide the world countries into two categories, developed and developing countries. This classification considers pre-developing countries and developing countries into one category (developing) while the proportion of people with fundamental energy needs in the former is far higher than in the latter. The objective of this paper is to allocate a separate class for pre-developing which their fundamental energy needs have not completely satisfied. The methodology of the study is divided into two parts. Initially, based on the longitudinal data (112 country’s data during the period of 2005–2013), a linear Quality of Life (QoL) indicator is proposed in terms of six variables. Then an S-shape (sigmoid) curve is fitted to the QoL indicator data against energy consumption (total primary energy supply as a proxy) per capita (ECpc) and electricity consumption per capita (Elcpc) data. Three types of countries, developed, developing, and pre-developing are identified based on the sigmoid function. The results of the proposed model demonstrate that the “pre-developing” category has different QoL and ECpc as compared to developing and developed classes. Another result of the paper shows the entry of new technologies has influenced the QoL and Elcpc to a greater extent in developing countries than in developed countries. One-way analysis of variance is a method to shed light on the latter consequence. In the case of pre-developing countries, the pertinent analysis shows an insignificant impact. This paper concludes the new classification of countries appropriately addresses variation of QoL against ECpc in each class. [ABSTRACT FROM AUTHOR]

Energy resource depletion and environmental degradation have become big challenges for China's sustainable development. Energy efficiency improvement is usually regarded as an major pathway for reducing the pressure on energy and the environment. However, this would be undermined by the rebound effect, because the rebound effect would partially or even totally offset the anticipated of efficiency improvement. Evidence of the rebound effect has important implications for the economic, resource and environmental consequences of improving energy efficiency, as well as enables policy makers to take informed decisions on energy and environmental issues. In this paper, we incorporate the endogenous energy efficiency into the model specification of rebound effect, and build a unified comprehensive framework for analyzing rebound effect and related issues. We applied this framework to conduct empirical analysis and comparison of heavy and light industries, considering heterogeneity across sub-industries. Furthermore, this paper goes beyond simple estimation of rebound effect, and takes additional effort to highlight the policy application via combining energy subsidies and technological progress with rebound effect. [ABSTRACT FROM AUTHOR]

The paper develops an electric train energy consumption modeling framework considering instantaneous regenerative braking efficiency in support of a rail simulation system. The model is calibrated with data from Portland, Oregon using an unconstrained non-linear optimization procedure, and validated using data from Chicago, Illinois by comparing model predictions against the National Transit Database (NTD) estimates. The results demonstrate that regenerative braking efficiency varies as an exponential function of the deceleration level, rather than an average constant as assumed in previous studies. The model predictions are demonstrated to be consistent with the NTD estimates, producing a predicted error of 1.87% and −2.31%. The paper demonstrates that energy recovery reduces the overall power consumption by 20% for the tested Chicago route. Furthermore, the paper demonstrates that the proposed modeling approach is able to capture energy consumption differences associated with train, route and operational parameters, and thus is applicable for project-level analysis. The model can be easily implemented in traffic simulation software, used in smartphone applications and eco-transit programs given its fast execution time and easy integration in complex frameworks. [ABSTRACT FROM AUTHOR]

The case study presented in this paper is an innovative urban roof-mounted energy system constituted by a hybrid solar system for domestic use. Utilizing this untapped energy is the key value for home renewable energy supply. It allows the improvement of the energy yield per area unit of roof or façade. The Photovoltaic/Thermal (PVT) panel considered presents the particularity of the addition of a transparent insulating cover to reduce the heat losses on its front side. It has been developed by the manufacturer Endef Engineering in collaboration with the University of Zaragoza. In this paper, the design of a PVT system to feed the domestic heat water requirements of multi-housing building is explained. The electricity production is also considered, in accordance with the Spanish regulation for self-consumption. The work developed began with the redesign of the hybrid solar plant, which individually supplies hot water to each dwelling and power for common consumption of the building, including a charging system for electric vehicles. Then, the PVT panel developed and manufactured by Endef Engineering and the complete thermal and electrical system are simulated in Trnsys. The monitoring of a real working installation is used in this paper to validate the proposed model. The presented case study is located in Zaragoza (Spain), in a residential apartment block, with an electrical installed power of 4.14 kWp and 20.5 kW of thermal capacity. The proposed model allows the calculation of the heat and electricity production and efficiency of the whole system with error lower than 6.5%. [ABSTRACT FROM AUTHOR]

This paper discusses the application of distributed energy resources (DERs) at water resource recovery facilities (WRRF). The DERs considered include solar photovoltaic (PV) systems, biomass electricity generation, and energy storage systems (ESS). The financial and demand management implications of the deployment of various DER combinations are analyzed. The load profile of a single plant is studied in order to draw conclusions regarding the optimal size for energy storage solutions based on the goals of the WRRF. A case study of a Medium sized WRRF, located in a large metropolitan area, is used to compare different financial and technical implementation strategies. An ideal combination of DERs is determined based on analysis of various scenarios for nine distinct use cases. The overarching goal of this paper is to understand the potential of various combinations of DERs for managing peak demand and resilience at WRRFs. Elements in the financial models are compared to determine the combination that creates a financially viable project while providing tangible benefits in resilience and/or demand management to the WRRF. • Energy efficiency is key for urban regions to combat global warming. • Distributed energy resources (DERs) improve resiliency during power failures. • Energy efficiency is possible through deployment of DERs and better use of biogases • Distributed energy resources (DERs) improve resiliency during power failures. [ABSTRACT FROM AUTHOR]

With the proposed concept of energy blockchain, peer-to-peer (P2P) energy trading between prosumers is regarded as a potential way for future power market development. However, existing P2P energy trading often has problems such as low trading efficiency and significant communication losses. This paper presents an energy trading strategy in the blockchain environment to solve the problems of P2P energy trading. First, the type of market is determined based on energy supply and demand. Then, a Stackelberg game is used to establish a two-layer model of the leader (upper) and follower (lower) to determine the price. In addition, we propose an energy trading method based on feasible regions. The trading strategy considers the supply and demand of the market, provides an accurate energy transfer signal, and can help energy blockchain achieve better "self-sufficiency". A case study proves the effectiveness of the strategy. Compared with the existing methods, the strategy proposed in this paper considers the market type and can promote the coordination and complementation of energy in the microgrid, and improve trading efficiency and reduce communication loss without sacrificing privacy. This study demonstrates that P2P power trading has brought benefits to prosumers, has promoted the development of the energy market, and has shown the massive potential of energy blockchain. • A P2P energy trading model and a consensus algorithm in the blockchain are proposed. • An interactive pricing-and-matching P2P energy trading strategy is proposed. • A novel P2P energy trading pricing model based on game theory is proposed. • A P2P energy trading matching method based on the feasible region is proposed. • Results show that the strategy proposed in this paper can optimize the microgrid. [ABSTRACT FROM AUTHOR]

Transactive Energy (TE) is envisaged as an advanced demand response (DR) variant to leverage the flexibility of distributed energy resources (DERs) for enhancing energy balance and network management in modern power systems. However, there have been limited implementations of TE frameworks for low voltage (LV) residential networks to capture the underutilised flexibility potential of DER-equipped residential prosumers. The main purpose of this paper is to identify the rationale behind this gap in light of recent advances in TE-based energy management for residential networks. As such, first, we identify the motivation and significance of the evolution of TE framework from traditional DR schemes by reviewing their relative efficacies in utilising demand-side flexibility of DER-rich residential networks for enhancing energy balance and local network management. Second, we provide an overview of the key components of the TE framework that are essential to facilitate active negotiation and trading of demand-side flexibility in residential networks. Third, we review the state-of-the-art TE methodologies and industry projects that have utilised demand-side flexibility of residential prosumers. Finally, several challenges relevant to TE frameworks in LV residential networks are identified followed by some concluding remarks at the end of the paper. • Demand flexibility potential is analysed for residential prosumers. • The evolution of transactive energy from demand response is rationalised. • A comprehensive overview of the transactive energy management framework is presented. • State-of-the-art grid-supporting transactive energy models are surveyed. • Key challenges of transactive energy for residential applications are identified. [ABSTRACT FROM AUTHOR]

Highlights • Parameters of 60 electricity-saving measures and 16 abatement measures are compiled. • Synergies of electricity use in China's chemical sector at grid level are assessed. • Efficient use of power can cut 154 Mt CO 2eq , 295 kt SO 2 , 322 kt NO x and 64 kt PM 2.5. • North and Northwest grids have the largest economic benefits on emissions reduction. • Grid with high electricity price is more sensitive to price change than other grids. Abstract Industry is a large electricity user. China's chemical industry (globally the largest based on sales) contributes 7% to China's GDP, while it consumes 11% of the total electricity consumption in industry and is responsible for 40% of total CO 2eq , 40% of SO 2 , 59% of NO x and 18% of PM-emissions of the chemical industry emissions. The heterogeneity of GHG and air pollutant emissions across electricity grids (within a country) is rarely included in analyses. In this paper, electricity conservation supply curves are developed (distinguishing the grids) to estimate the cost-effective and technical potentials of electricity conservation in China's chemical industry. The emission factors per grid for GHG (i.e. CO 2 , CH 4 and N 2 O) and air pollutants (i.e. SO 2 , NO x and PM 2.5) are calculated and used to quantify the emissions mitigation achieved by electricity saving technologies in the chemical industry for the period 2012–2035. Results show that significant multiple benefits can be obtained by implementing electricity efficiency measures. There are large differences among the six grids in terms electricity savings and emissions abatement of GHG and air pollutants. 83% of the total electricity saving potential is contributed by the North, Northwest and Central grids, equal to 32% of baseline electricity consumption in 2035. In 2035, 129 Mt of CO 2 , 33 kt CO 2eq of CH 4 , 571 kt CO 2eq of N 2 O, 235 kt of SO 2 , 275 kt of NO x and 52 kt of PM 2.5 in these three grids can be avoided as a result of electricity savings (a reduction of 31–33% compared to baseline emissions). When decision-makers set targets for energy saving and emission reduction, the multiple benefits and grid heterogeneity should not be ignored. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Proposes a conditional depreciation balancing strategy. • Proposes a novel integrated virtual battery model. • Proposes a multiscale searching algorithm to equalize the conditional depreciations. • The energy storage units with different energy features are utilized fully. • The life depreciation rate of extended hybrid energy storage systems is decreased. Abstract Various history life depreciations and energy characteristics of the facilities in the hybrid energy supply system challenge the conventional balancing strategies, which mostly aim to address the state-of-charge imbalance issues. This paper proposes a conditional depreciation balancing strategy to cope with the stated challenge. The conditional depreciation balancing strategy is based on extended hybrid energy storage systems, which consist of supercapacitors, batteries and equivalent battery storage units corresponding to heating systems. An integrated virtual battery model is adopted in the equivalent process of the heating system. The conditional depreciation is innovatively defined to evaluate the operation equitability of the extended hybrid energy storage systems. The equalization of the conditional depreciation is achieved by a multiscale searching algorithm. The proposed conditional depreciation balancing strategy is validated by a hybrid energy supply system of a four-floor building in Shanghai, China. Test results show that the fast dynamic response ability of the supercapacitors and the high energy density of the integrated virtual battery models are utilized fully by the proposed conditional depreciation balancing strategy. The maximum power changing rate of the supercapacitors, integrated virtual battery models and batteries is 0.86 kW/s, 0.03 kW/s, and 0.22 kW/s, respectively. The 24-h charging/discharging energy of the supercapacitors, integrated virtual battery models and batteries is 7.80 kWh, 18.37 kWh and 5.21 kWh, respectively. Compared to the state-of-charge balancing strategy, the proposed conditional depreciation balancing strategy decreases the maximum imbalance coefficient of the energy storage units by 82.74%. The life depreciation rate and the life depreciation cost of the extended hybrid energy storage system decrease by 47.05% and 53.85%, respectively. [ABSTRACT FROM AUTHOR]

Highlights • Develop a new functional model to forecast day-ahead natural gas-flow in Germany. • Incorporate functional exogenous variables in the modeling. • Strong evidence that the proposed model outperforms several alternative models. • Improve forecast accuracy by up to 5-fold. • Results show that nominations and lagged 1-day flow are important predictors. Abstract Forecasting natural gas demand and supply is essential for an efficient operation of the German gas distribution system and a basis for the operational decisions of the transmission system operators. The German gas market is moving towards more short-term planning, in particular, day-ahead contracts. This increases the difficulty that the operators in the dispatching centre are facing, as well as the necessity of accurate forecasts. This paper presents a novel predictive model that provides day-ahead forecasts of the high resolution gas flow by developing a Functional AutoRegressive model with eXogenous variables (FARX). The predictive model allows the dynamic patterns of hourly gas flows to be described in a wide range of historical profiles, while also taking the relevant determinants data into account. By taking into account a richer set of information, FARX provides stronger performance in real data analysis, with both accuracy and high computational efficiency. Compared to several alternative models in out-of-sample forecasts, the proposed model can improve forecast accuracy by at least 12% and up to 5-fold for one node, 3% to 2-fold and 2-fold to 4-fold for the other two nodes. The results show that lagged 1-day gas flow and nominations are important predictors, and with their presence in the forecast model, temperature becomes insignificant for short-term predictions. [ABSTRACT FROM AUTHOR]

• A regional integrated energy system with multi-region energy sharing and storage is constructed. • A co-optimization method compromise system configuration and operation dispatch is proposed. • The overall benefit of the RIES in a nearly-zero energy region scenario is studied. • The role played by energy sharing and storage in renewable energy utilization is discussed. The nearly-zero energy community composed of contiguously nearly-zero energy buildings can effectively reduce the operational energy consumption of buildings by adopting appropriate active and passive technologies, which is the main development direction of future buildings. However, there are few studies on the collaborative optimization of nearly-zero energy community supply systems. Therefore, a nonlinear cooperative optimization model of the regional integrated energy system with multi-region energy sharing and multi-energy storage is constructed in this paper. A two-layer collaborative optimization method is proposed, which optimizes the upper layer's renewable energy and energy storage capacity and the operation dispatching in the underlayer. Finally, the overall benefit, typical daily energy scheduling, and the energy sharing and storage impact on renewable energy utilization of the system when it supplies energy to a nearly-zero energy community are studied. The research results show that compared with the isolated integrated energy system, the supply cost, primary energy consumption, carbon emission and interactive power per unit area of the regional integrated energy system are reduced by 3.45 CNY/m2, 3.95 kWh/m2, 1.35 kg/m2 and 1.66 kWh/m2, respectively. In addition, multi-region energy sharing and multi-energy storage can effectively improve the self-consumption/sufficiency ratio of photovoltaic and wind power generation and solar thermal collector heat collection. This study provides a feasible solution for applying nearly-zero energy communities in regional integrated energy systems. [ABSTRACT FROM AUTHOR]

Despite their advantages, distributed energy resources (DERs) bring inherent uncertainty and variability into the landscape of modern power systems. As DER penetration grows, conventional generators like hydropower plants have to respond more often to arrest the imbalance in the net load. Hydropower turbines provide their best operational performance with minimal wear and tear when operating at regions of maximum efficiency. However, the current needs for hydropower plants require them to operate under varying load conditions and thus sub-optimal operating points leading to additional stress. To relieve the hydropower plants, this paper proposes a hybridization strategy where a hydropower unit is paired with an energy storage system (ESS) to increase operational flexibility and mitigate damage to the hydro plant. Models are developed to represent the operation of the hybrid system, quantify degradation, and assess economic benefits. Moreover, an innovative controller disaggregates the market dispatch signal into separate control setpoints for the ESS and hydropower unit. In case studies performed on a real-world hydropower facility, it was found that the ESS-based hybridization can extend the life of the hydropower plant by 5% on average. The economic benefits from reduced maintenance and deferred investment are estimated to be around $3.6 million. • A hybridization strategy is proposed to pair energy storage with a hydro plant. • Energy storage helps increase flexibility and mitigate damage to the hydro plant. • Methods are developed to model the hybrid system and quantify degradation effects. • An innovative controller is designed to optimize the set points by component. • Economic benefits from reduced maintenance and deferred investment are assessed. [ABSTRACT FROM AUTHOR]

Threats against power grids continue to increase, as extreme weather conditions and natural disasters (extreme events) become more frequent. Hence, there is a need for the simulation and modeling of power grids to reflect realistic conditions during extreme events conditions, especially distribution systems. This paper presents a modeling and simulation platform for electric distribution grids which can estimate overall power demand during extreme weather conditions. The presented platform's efficacy is shown by demonstrating estimation of electrical demand for (1) Electricity Reliability Council of Texas (ERCOT) during winter storm Uri in 2021, and (2) alternative hypothetical scenarios of integrating Distributed Energy Resources (DERs), weatherization, and load electrification. In comparing to the actual demand served by ERCOT during the winter storm Uri of 2021, the proposed platform estimates approximately 34 GW of peak capacity deficit. 1 1 These numbers are consistent with state-of-the-art prediction results published in the literature (Gruber et al., 2022). For the case of the future electrification of heating loads, peak capacity of 78 GW (124% increase) is estimated, which would be reduced to 47 GW (38% increase) with the adoption of efficient heating appliances and improved thermal insulation. Integrating distributed solar PV and storage into the grid causes improvement in the local energy utilization and hence reduces the potential unmet energy by 31% and 40%, respectively. • Modeling and simulation platform to estimate power demand during extreme weather conditions. • Demonstration of the platform on 2021 winter storm Uri conditions. • 34 GW of peak capacity deficit estimated for the current Electricity Reliability Council of Texas (ERCOT) grid. • 78 GW peak capacity deficit for load electrification, reduced to 47 GW with technology efficiency • Integrating distributed solar PV and storage reduces the potential unmet energy by 31% and 40%, respectively. [ABSTRACT FROM AUTHOR]

• This paper proposes a joint energy control model for microgrid and manufacturing. • Markov decision process is used to model the decision procedure. • Reinforcement learning leveraging TD and DPG is proposed to solve the problem. Microgrid is a promising technology of distributed energy supply system, which consists of storage devices, generation capacities including renewable sources, and controllable loads. It has been widely investigated and applied for residential and commercial end-use customers as well as critical facilities. In this paper, we propose a joint state-based dynamic control model on microgrids and manufacturing systems where optimal controls for both sides are implemented to coordinate the energy demand and supply so that the overall production cost can be minimized considering the constraint of production target. Markov Decision Process (MDP) is used to formulate the decision-making procedure. The main computing challenge to solve the formulated MDP lies in the co-existence of both discrete and continuous parts of the high-dimensional state/action space that are intertwined with constraints. A novel reinforcement learning algorithm that leverages both Temporal Difference (TD) and Deterministic Policy Gradient (DPG) algorithms is proposed to address the computation challenge. Experiments for a manufacturing system with an onsite microgrid system with renewable sources have been implemented to justify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Integration of different energy resources at the customers’ premises in recent years, advances in Information and Communication Technologies ( ICT s), and Advanced Metering Infrastructure ( AMI ) systems are becoming attractive tools for developing new real time demand response at the supplier side and the management of energy resources at the customers’ side. The management programs can be classified as; smart grid management from the supplier side and intelligent Energy, ‘‘ i - Energy ”, from the consumer energy management side. There are two types of programs; (i) time based program like real time pricing and (ii) incentive based demand response. A combination of these two programs is proposed in this paper with the merged program to be real time with incentive demand response. The time based demand response programs can be improved by using smart metering infrastructure and different resources. The incentive demand comes from the feasibility of providing new concept known as ‘i-energy’ at the customers’ sides. To achieve this, Smart Meters ( SMs ) and different resources at the customers’ premises using this concept are applied. By integrating different resources at the customers’ premises, using the i-energy concept, can change the limitation given in the time based program. The first developed program at the supplier side depends on purchasing MW from the customers who participate in the program. The second contribution is the ‘‘ i - Energy ” management technique at the customers’ side that is based on congestion and potential games through strategy of load control using different resources. Revenue for different participants in the program from the commercial and industrial sectors, at different levels of reduction and different usage of different resources, is discussed. [ABSTRACT FROM AUTHOR]

The key distinction between production-based and consumption-based accounting lies in the way in which trade-embodied emissions are allocated between producer and consumer regions. Yet, these two regions are not necessarily directly connected to each other in supply chains, due to spatial production fragmentation. To better understand the link from the production-based to the consumption-based regional carbon inventories, this paper defines the economic length of carbon transfer (ELCT) as the number of administrative border-crossing involved in the influence process of a region’s final demand on the carbon emissions of another region. Using a multi-regional input-output analysis framework for China, we propose an approach to calculating the ELCT related to a region’s production or demand for domestic consumption or international export. We find that the average ELCT in 2010 is 1.34 and that more than a quarter of interregional carbon transfer crosses provincial borders twice or more. The provincial and sectoral analysis of ELCT reveals that spatial production fragmentation is an important challenge to be conquered for successful implementation of consumption-based climate regulations. [ABSTRACT FROM AUTHOR]

Understanding the energy metabolism of national economies is nowadays crucial for policymakers in order to define effective policies and to properly set energy efficiency targets. Energy accountings based on the traditional Production-based accounting method (PBA) allows to understand how primary energy is directly extracted, traded, transformed and used within each economy. On the other hand, Consumption-based accounting method (CBA) allows to understand the ultimate economic purposes of such energy flows. The information provided by the joint application of these approaches may provide useful and complementary insight on the national energy metabolism, allowing to identify hotspots for potential interventions from both the supply and demand side. This paper reviews PBA and CBA energy accounting methods, presenting a possible way for the joint use of their results, consistently represented by means of one unique Sankey diagram. This will be useful to have a comprehensive insight on the energy metabolism of national economies, supporting analysts and policymakers in the identification of energy efficiency hotspots. The method is applied to South Africa and to the neighbor country of Botswana, based on data provided by IEA energy statistics and the EORA26 Multi-Regional Input-Output model, taking into account non-renewable fossil energy (raw coal, crude oil and natural gas) and considering the reference year 2013. Results suggest that the joint use of PBA and CBA methods may provide useful information on the hidden energy links among national economies, helping analysts and policymakers in defining alternative energy efficiency policies. For this reason, the Authors argue that results of CBA should be provided alongside energy statistics based on the traditional PBA approach. [ABSTRACT FROM AUTHOR]

The electric power, gas, and heat systems work on different but complementary time and space scales. Multiple energy systems (MES) yields an increase on the efficiency and flexibility of energy supply. The coupling between different forms of energy bring difficulties in the planning of the overall energy system. This paper proposes a novel optimal planning method for a community level MES that jointly determines the optimal generation, conversion and delivery of electricity, heat, cooling, and other services. First, configuration planning of a community level MES is introduced and defined using the energy hub (EH) concept. Then, the planning problem is presented with the objective of minimizing the sum of the investment and operating costs, with variables that represent the selection of energy converters or storage and their relationships. The model is then formulated as a mixed-integer linear programming (MILP) problem based on graph theory. The proposed model does not requires any pre-assumptions on the configurations of the EH so that it can plan the MES starting from scratch. Finally, an illustrative example is provided to describe the functioning of our proposed method. A numerical case study for the planning of a subsidiary administrative center in Beijing, China is presented to demonstrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

• Nonlinear characteristics of lithium-ion battery are modeled with EECM and Peukert's law. • Load profiles are analyzed by energy and power demands and fluctuation characteristics. • Ageing effects of operating condition is considered by the fluctuation feature of load profile. • Adaptive deterministic approach is used to obtain optimal size of the battery system. • Proposed battery system size optimization is applied and verified in EVs and PV generators. For achieving decarbonization globally, most electric applications, such as electric vehicles (EVs) and photovoltaic (PV) generators, are moving toward the use of eco-friendly and renewable energy sources. Currently, one of the most utilized energy storage devices is the lithium-ion battery, which has various advantages than other systems. In addition, a lithium-ion battery presents nonlinear characteristics during operation. Therefore, these features should be considered in the design of battery systems with load requirements. In this paper, energy and power characteristics were analyzed in terms of battery system size in relation to EVs and PV generators. If a battery system is designed to supply power less than the energy demand, it will be utilized beyond the designed and safety range, and there will be generation of waste residual energy. However, if a battery system is designed to supply power more than the demand, additional investment and maintenance cost will be incurred to setup the battery system. Thus, this paper proposes an optimal sizing method for a lithium-ion battery system considering its nonlinear features using Peukert's law and the analyzed load conditions. To obtain the ideal size of the battery system, an adaptive deterministic approach is implemented. After determining the optimal battery system size, a simulation of the battery system with a load profile is conducted to verify that it satisfies all constraints of the battery system. [ABSTRACT FROM AUTHOR]

• The impact of economic growth pressure on energy efficiency improvement is discussed. • Economic growth pressure has hindered the improvement of energy efficiency. • Heterogeneity effects exist in individual dimension and temporal dimension. Economic growth target management has been a typical feature of Chinese governments at all levels for a long time in the past decades. Based on a panel data of 188 Chinese cities, this paper examines the impact of pressure brought by economic growth target on energy efficiency improvement. The results suggest that economic growth pressure has hindered the improvement of energy efficiency. For every unit of increase in economic growth pressure, the improvement indicator of energy efficiency will decrease by 3.3%. This finding is robust, after replacing explained variables and core explanatory variables, no substantial changes are found. Sub-sample regression confirms the existence of heterogeneity effects in individual dimension and temporal dimension. Finally, by summarizing existing studies, this paper finds that the investment imbalance effect, the hindrance effect on industrial structure upgrading, and the crowding-out effect on innovation are three important ways for economic growth pressure to restrain the improvement of energy efficiency. Our findings are helpful to understand the role of the government's economic growth target management in resource allocation and energy utilization and provide a certain practical reference for China to promote high-quality economic development. [ABSTRACT FROM AUTHOR]

• Four environmental indicators suitable for use in energy system models are proposed (85) • The indicators allow direct comparison of decarbonisation potential of renewables (84) • Gearbox double-fed induction generators are the preferred form of wind turbine (80) • Cadmium telluride photovoltaics exhibit best overall raw materials outcomes (78) Raw materials and their related environmental impacts will play a key role in the implementation of renewable energy infrastructures for decarbonization. Despite the growing amount of data quantifying raw materials for energy production technologies, few examples of these data sources are being included in current energy system models. Accordingly, this paper introduces possible pathways for integrating material-specific life cycle assessment outputs and material metabolism indicators into energy system models so that raw material requirements, and their associated impacts, can be accounted for. The paper discusses the availability of life cycle inventories, impact assessment methods and important output indicators. The material metabolism indicators most relevant to the current policy debate surrounding the European Green Deal–namely, material supply risk and contribution of recycled materials to total supply–are also discussed alongside the value of adding this information to energy system models. A methodology for using data from both approaches is offered and operationalised using four sub-technologies of both wind turbines and solar photovoltaic panels as case studies. The results show that considerable variation exists between and within the two groups for all indicators. The technologies with the lowest global warming potential, cumulative energy demand and supply risk are turbines with gearbox double-fed induction generators and cadmium telluride photovoltaics. Furthermore, wind turbines exhibit significantly higher recycling rates than photovoltaics. Ultimately, the integration of such methodologies into energy system models could greatly increase the awareness of raw material issues and guide policies that maximise compatibilities between resource availability and cleaner energy systems. [ABSTRACT FROM AUTHOR]

The increasing penetration of distributed energy resources (DERs) enable integrated community energy systems (ICESs) as emerging techno-economic energy entities at the end-user side. In the deregulated electricity market, the ICESs can actively participate in the distribution electricity market (DEM) to provide active and reactive power dispatched by ICES operator (ICESO). In this context, a strategic active and reactive power scheduling model for ICESs in the day-ahead DEM is proposed in this paper. A novel trading mechanism designed for ICESO and distribution system operator (DSO)'s interaction toward both active and reactive power is represented in the day-ahead DEM, where distribution locational marginal prices (DLMPs) for both active and reactive power are introduced as the market settlements. Then, the trading process is formulated as a bi-level programming problem. The upper level describes the combined cooling, heating, and power (CCHP)-dominated ICESO's strategic active and reactive power dispatch, in which the DLMPs for active and reactive power, internal flexibility services, and optimal dispatch for inverter-based distributed generators are considered. The lower level performs a DEM clearing for both active and reactive power. Further, Karush–Kuhn–Tucker (KKT) conditions, Big-M approach, and duality theory are used to convert the bi-level model from a Mathematical Programing with Equilibrium Constraints (MPEC) model to a single-level mixed-integer second-order cone programming (MISOCP) model for efficient calculation. Finally, case studies on modified IEEE 33-node and IEEE 123-node distribution systems are adopted to evaluate the performance of the proposed scheduling method. The results show that the proposed scheduling approach can contribute to the renewable energy share and energy efficiency, reduce CCHP-dominated ICESs' operation costs by 38.13% and 13.21% compared to the case where ICESO only acts as a price-taking consumer, and 6.64% and 3.44% compared to the case where ICESO does not consider reactive power bids/offers, respectively. [ABSTRACT FROM AUTHOR]

Transactive energy systems use dynamic electricity markets to present an improved way to control large numbers of distributed energy resources while maintaining user privacy, autonomy and scalability. However, this access to dynamic electricity prices can introduce negative emergent behavior like high peak loads if not adequately regulated by grid operators. To solve this, this paper proposes a regulated peer-to-peer market structure for residential prosumers. Prosumers bid energy demand or supply curves into a peer-to-peer market, which can then be directly used by the utility to quantify and dispatch demand flexibility. A utility surcharge mechanism can then be employed to dispatch this flexibility, and achieve significant improvements to reliability. This framework is tested in a simulation environment of electrified residential buildings with significantly enhanced fidelity representing the real-world housing stock. New York State and the month of January was chosen for the demonstration purpose, considering that January is one of the most challenging months for fully electrified systems in cold climates as solar production occurs at opposite times of the highest heating load. Results show that the surcharge can reducing peak load and ramp rates by nearly 50%, while also encouraging consumption of locally generated electricity. • Unregulated peer-to-peer electricity markets neglect grid network constraints. • These markets can be regulated by a surcharge imposed by the grid operator. • This surcharge dispatches demand flexibility in a predictable, quantifiable manner. • Decentralized demand flexibility is then quantified by aggregating demand bid curves. • This regulation method is decentralized, scalable, and privacy-preserving. [ABSTRACT FROM AUTHOR]

Although electric power networks and district heating networks are physically coupled, they are not operated in a coordinated manner. With increasing penetration of renewable energy sources, a coordinated market-based operation of the two networks can yield significant advantages, as reduced need for grid reinforcements, by optimizing the power flows in the coupled systems. Transactive control has been developed as a promising approach based on market and control mechanisms to coordinate supply and demand in energy systems, which when applied to power systems is being referred to as transactive energy. However, this approach has not been fully investigated in the context of market-based operation of coupled electric power and district heating networks. Therefore, this paper proposes a transactive control approach to coordinate flexible producers and consumers while taking into account the operational aspects of both networks, for the benefit of all participants and considering their privacy. A nonlinear model predictive control approach is applied in this work to maximize the social welfare of both networks, taking into account system operational limits, while reducing losses and considering system dynamics and forecasted power supply and demand of inflexible producers and consumers. A subtle approximation of the operational optimization problem is used to enable the practical application of the proposed approach in real time. The presented technique is implemented, tested, and demonstrated in a realistic test system, illustrating its benefits. • Transactive control design for coupled electric power and district heating networks. • Development and application of an accurate and computationally efficient system model. • Technical and economic optimal system operation considering participant privacy. [ABSTRACT FROM AUTHOR]

Exergy Life Cycle Assessment (ELCA) is proposed by literature to account for the exergy embodied in products of energy systems. In order to make results of ELCA comparable, supply chains that support the life cycle of the system should be analyzed through a unified model: this is one of the main concerns related to Life Cycle Assessment and Industrial Ecology disciplines. In this paper, Hybrid Input-Output analysis is proposed as the computational structure of ELCA: according to this method, national supply chains are modeled through the Monetary Input Output Tables (MIOTs) of national economies, a constantly updated and freely available data source. Then, the adopted national MIOT is expanded to include the detailed model of the considered energy system, hence defining a Hybrid Input-Output model. The (non-renewable) exergy embodied in electricity production and the Exergy Return on Investment (ExROI) are defined as the appropriate performance indicators based on ELCA. The introduced model is here adopted for the analysis of a Waste-to-Energy (WtE) power plant currently operating in the Italian context. It is found that the primary non-renewable exergy embodied in electricity produced by the analyzed WtE is non-negligible for both the construction (127.1 toe) and the operation phases (11.6 toe/y). Nonetheless, the plant is able to produce a net amount of electricity that pays back such primary non-renewable resources requirements about a hundred times. Finally, the joint application of Exergy Analysis and ELCA lead to improve the overall thermodynamic performances of the WtE system, increasing its exergy efficiency by 1%, and reducing the non-renewable exergy embodied in electricity production by 7938 toe. [ABSTRACT FROM AUTHOR]

The EU dependence on imported gas is increasing, rising to 67% in the year 2014 with 30% of total gas consumption used for electricity generation that year. With such a dependence on imported gas, gas supply interruptions can have significant impacts on the EU energy system and economy. This points to the need for integrated electricity and gas modelling tools to fully explore the potential impacts of gas supply interruptions. This paper builds and applies a detailed publically available integrated electricity and gas model for the EU-28. We use this model to examine a number of hypothetical scenarios where gas supply routes are interrupted for yearly periods and the impacts on power system operation and gas flow in Europe observed. Model results show that interruption of Russian gas supply to the EU could lead to a rise in average gas prices of 28% and 12% in electricity prices. When supply from North Africa was removed all Southern European states were affected heavily, Spain in particular saw large increases of 30% in gas prices with a corresponding rise of 18% in electricity prices as a result. In addition to supply interruptions, all gas storages were removed from the model to examine the importance of gas storage infrastructure. This resulted in an average increase in power prices of 6% across Europe. These additional insights offer an increased understanding of the interplay between the gas and power systems and identify challenges which may arise when seeking to understand energy systems as a whole. [ABSTRACT FROM AUTHOR]

Microgrids are small scale power systems with local resources for generation, consumption and storage, that can operate connected to the main grid or islanded. In such systems, optimal sizing of components is necessary to ensure secure and reliable energy supply to loads at the least cost. Sizing results are however dependent on the energy management strategy used for operating the system, especially when components with different dynamics are considered. Results are also impacted by uncertainty on load as well as renewable generation. In this paper, we propose a combined sizing and energy management methodology, formulated as a leader-follower problem. The leader problem focuses on sizing and aims at selecting the optimal size for the microgrid components. It is solved using a genetic algorithm. The follower problem, i.e., the energy management issue, is formulated as a unit commitment problem and is solved with a mixed integer linear program. Uncertainties are considered using a form of robust optimization method. Several scenarios are modeled and compared in simulations to show the effectiveness of the proposed method, especially with respect to a simple rule-based strategy. [ABSTRACT FROM AUTHOR]

• Analyzing various architectural function ratio combinations of urban complex. • Evaluating energy performance of urban complex with various function combinations. • Providing function scheme data set of urban complex in terms of energy performance. • Improving pre-programming process of urban complex by considering energy factor. In the context of the global energy shortage, urban complexes have attracted more and more attention from energy-saving research due to their large number and great energy-saving potential. While, the energy performance factors are rarely considered in the function scheme decision of urban complex in the recent pre-programming process. Optimizing the building area ratio scheme of urban complex is the key link to improve the comprehensive energy utilization efficiency. This paper proposes an approach to optimize building area ratio scheme of urban complex from energy performance perspective by energy simulation, scenario analysis and demand side management methods, and provides a data set of rankings of urban complex function combination schemes by evaluating the performance of energy load leveling and the degree of load stability of each building area ratio scheme in different climatic conditions, respectively. The results indicate that: 1) the different building area ratios notably affect the integrated operating energy performance of urban complexes with a maximum evaluation indexes difference of more than 85%; 2) the evaluating and optimizing results of the building area ratio schemes show broadly similarity in different climate conditions; 3) the optimal building area ratio scheme of urban complex is usually with less "shopping mall" (0 ∼ 10%), more "residence" (more than 35%), and floating ratio of "office" and "hotel" (0 ∼ 35%). The main findings of this study provide a quantitative basis for the architectural pre-programming and function scheme decision-making of urban complex by demand-side energy management to improve the energy efficiency, so as to guarantee the energy supply security. [ABSTRACT FROM AUTHOR]

The use of data envelopment analysis (DEA) in China’s regional energy efficiency and carbon emission efficiency (EE&CE) assessment has received increasing attention in recent years. This paper conducted a comprehensive survey of empirical studies published in 2006–2015 on China’s regional EE&CE assessment using DEA-type models. The main features used in previous studies were identified, and then the methodological framework for deriving the EE&CE indicators as well as six widely used DEA models were introduced. These DEA models were compared and applied to measure China’s regional EE&CE in 30 provinces/regions between 1995 and 2012. The empirical study indicates that China’s regional EE&CE remained stable in the 9th Five Year Plan (1996–2000), then decreased in the 10th Five Year Plan (2000–2005), and increased a bit in the 11th Five Year Plan (2006–2010). The east region of China had the highest EE&CE while the central area had the lowest. By way of conclusion, some useful points relating to model selection are summarized from both methodological and empirical aspects. [ABSTRACT FROM AUTHOR]

This paper presents a modeling framework to address the energy, economy, emissions and land use nexus when exploiting bioenergy in developing countries. The modeling framework combines a qualitative and a quantitative element. The qualitative element integrates two components: (1) technology roadmapping to identify long-term technology targets through expert judgment and (2) scenario analysis to investigate different future storylines. The quantitative element comprises four integrated tools, namely the energy system model (ESM), the land use and trade model (LUTM), an economic model, and an external climate model. An overview of the modeling framework, scenario analysis, structure of the models, modeling techniques, mathematical formulations and assumptions is presented and discussed. The modeling framework is applied to the particular context of Colombia, as a case study of a developing country with large bioenergy potential. In this study case, the impacts that an accelerated deployment of bioenergy technologies might cause on the energy demand and supply, emissions and land use until 2030 are evaluated. Results suggest that a plan to exploit bioenergy in Colombia should prioritize the deployment of technologies for biomethane production, power generation & CHP, which can reduce more GHG emissions and more emissions per incremental hectare of land than first-generation biofuels. Moreover, while the share of bioenergy in the primary energy demand decreases in all the analyzed scenarios, it is possible to envision significant increases in the share of bioenergy in road transport energy demand, power generation and natural gas supply for scenarios implementing roadmap goals. In addition, impacts of El Niño oscillation on the dependence of hydro for power generation can be partly mitigated by exploiting the complementarity of hydro and bioenergy, which might result in a reduction of up to 5–6% in the demand for fossil fuels used in power generation in dry years. However, despite the ambitious goals proposed here, bioenergy alone cannot significantly reduce emissions by 2030 (maximum 10% reduction relative to baseline) and effective climate change mitigation requires a portfolio of additional measures. [ABSTRACT FROM AUTHOR]

• EQC is used to unify the heterogeneous energy in the form of exergy. • Equal exergy replacement mechanism for unification of heterogeneous energy in value. • Exchange any form of energy anytime for customers to reduce the energy cost. • Uncertainty of price quantified by IGDT to reduce the risk of VES. Integrated Demand Response (IDR) is an effective way to make full use of the resources of the integrated energy system (IES) to achieve a positive interaction between supply and demand. Energy cascade utilization is one of the important technical principles of the IES to improve energy utilization efficiency and realize comprehensive benefit optimization. The design of an incentive mechanism is an important prerequisite for realizing IDR. When formulating the incentive mechanism, the coupling between the conversion efficiency of multiple energy sources and the depreciation of energy quality in the process of energy cascade utilization should be comprehensively considered. The exergy is the index that can evaluate the engineering value of any form of energy. Therefore, this paper unifies the multiple heterogeneous energy sources in the IES in the form of exergy through the energy quality coefficient. To more accurately reflect the difference in energy value of multiple heterogeneous energy sources during the time of invocation, an equal exergy replacement mechanism is designed according to the principle of high energy quality and high price. The regulated energy of the customer participating in the IDR is stored in the form of exergy, and the customer can exchange any form of energy at any time under the same exergy. On this basis, a bi-level optimal scheduling model for virtual energy station (VES) to participate in the day-ahead market is established, which realizes the improvement of multi-subject interests. And use Information Gap Decision Theory (IGDT) to deal with the risks posed by uncertainty in day-ahead market prices. Finally, the case study shows that the designed incentive mechanism can further reduce the energy purchase cost of customers, and VES can obtain higher benefits than only providing internal energy sales services. The IGDT method can reduce operational risk, which verifies the feasibility of the method. [ABSTRACT FROM AUTHOR]

[Display omitted] • The CO 2 capture rate of liquid-solvent DAC is strongly influenced by air temperature and relative humidity. • Varying CO 2 capture rates have a strong impact on overall energy demand of DAC and levelized cost of CO 2 removal. • Levelized cost of capture can vary by a factor of nearly two between cold, dry conditions and warm, humid conditions. • GHG intensity of energy supply is, however, more influential than climate conditions in determining efficiency of removal. • Estimates of DAC potential should consider specific climate conditions rather than relying on generic performance figures. Direct air capture (DAC) is increasingly seen as a critical technology to reach mid-century net-zero targets and limit climate change to well below 2 °C. While the commercialization of DAC technologies is being pursued by numerous companies, there remains remarkably little information on their performance under "real-world" conditions. In this paper, for the first time, we investigate the influence of temperature and relative humidity of air on the CO 2 capture rate at the air contactor, overall energy requirement, CO 2 capture efficiency, and levelized cost of liquid-solvent based DAC systems. We observe that the overall energy demand decreases from 11.1 to 8.3 GJ/tCO 2 as the CO 2 capture rate increases from 40 to 85 % and that high capture rates can only be achieved in hot and humid climate conditions. We observe that a CO 2 capture rate of 75 % is only possible above 17 °C and 90 % relative humidity, and this drops dramatically at lower temperatures. It is also observed that water evaporation in the air contactor is highest at dry and low relative humidity, as expected. The sensitivity analysis showed that CO 2 capture efficiency is relatively insensitive to climate conditions for the liquid-solvent based DAC plant. Lastly, the levelized cost of natural gas standalone scenario varies from $240/tCO 2 to $409/tCO 2 , and this is more sensitive to temperature than relative humidity. The levelized cost of the natural gas stand-alone case is between 7 % and 10 % lower than that of an electric grid-connected case across all climate conditions. [ABSTRACT FROM AUTHOR]

The promotion of carbon neutrality targets will significantly influence the energy management of multi micro energy grid (MMEG) systems that can supply flexible local loads using various distributed energy resources. So far, few works incorporate the carbon emissions management with the fully-distributed coordination of MMEG systems to protect the environment and individual privacy and improve the energy efficiency. To fill this gap, in this paper, a hierarchical optimization framework is proposed for the MMEG system to solve an environmental economic dispatch problem, such that the energy coordination strategy could be obtained in a fully-distributed way. At the upper level, under a local and time-varying communication network, we develop an aggregate model based on a real-time price policy for the MMEG system. On this basis, a fully distributed consensus-based gradient projection method is proposed to minimize the total system generation cost with the coupled emission constraint satisfied, wherein a piecewise linearization technique is applied to handle the non-convexity and non-smoothness of the cost function caused by valve-point loading effects (VPLE) of thermal units. At the lower level, we apply an accelerated distributed augmented Lagrangian (ADAL) method to deal with a cost minimization optimization problem for users, such that each user could implement its own optimal strategy simultaneously based on the obtained aggregated strategy from the upper level. The effectiveness of the proposed approach is demonstrated by numerical simulations on an MMEG test system. • Coordinated optimization of multi micro energy grids improves the energy efficiency. • Coupled quadratic carbon emission constraints foster utilization of renewable energy. • Hierarchical framework achieves the system aggregation and individuals' autonomy. • Consensus-based gradient projection performs microgrids' fully-distributed operation. • Accelerated distributed Lagrangian method enables the local decision-making of users. [ABSTRACT FROM AUTHOR]

[Display omitted] • A model is proposed to maximize market performance including tradable certificates. • The long-term impacts of regulations on market are discussed by fine and subsidy. • Three important objectives for government are investigated under four scenarios. • The obtained results are validated by Turkey's energy data as a real case study. • Fine policy is more effective than a subsidy to empower renewable electricity. In the last decades, due to the increasing environmental concerns such as energy-resource depletion and global warming, energy efficiency instruments and renewable energy production has been promoted more than ever. This paper investigates a comprehensive comparison between two energy markets, i.e., the tradable white certificate (TWC) market and the tradable green certificate (TGC) market. How to coordinate the two energy markets and what the coordination impacts become the main issues under a reward-penalty mechanism. This multi-agent problem is discussed in an electricity supply chain consisting of two populations of traditional energy suppliers who want to decide about using one of them under government intervention. In this regard, the behavior of the suppliers is analyzed under three different Scenarios, including traditional power generation, energy efficiency program (EEP), and renewable portfolio standards (RPSs). An evolutionary game is applied to formulate the problem. The results show that a mandatory fine policy is more useful than an energy-saving subsidy to motivate the traditional suppliers to move toward sustainable energy generation. This policy also helps governments to meet their sustainable-oriented goals, as we discussed in this work. The findings can be applied to extend sustainable-based energy projects in developing countries. [ABSTRACT FROM AUTHOR]

• A realistic and integrated PSH and CH model is proposed for optimum energy management. • PSH balances energy supply and demand for intermittent renewable energy. • PSH sites with added water streams feeding upper reservoir incur net positive generation. • PSH reduced pumping energy consumption and enhanced off-peak annual energy production. The energy sector contributes to around 60% of total greenhouse gas emissions. To limit climate change, the world is going through an energy transition to clean and affordable energy, therefore the share of variable renewable energy is increasing day by day. Power systems require significant flexibility to operate reliably. Pumped storage hydropower allows load balancing and stable integration of intermittent renewable energy in the electrical grid. All energy storage technologies, including pumped storage hydropower, are considered a net negative contributor to the grid since they draw more energy than they deliver. This paper uniquely investigates the true potential of pumped storage hydropower and its optimum operation along with existing conventional hydropower. It considers power, energy production, and utilization for both installations to determine an equilibrium condition for a net positive energy balance. The case study of the 300 MW Balakot conventional hydropower plant in Khyber Pakhtunkhwa, Pakistan indicates that the pumped storage hydropower sites, where additional water streams reach the upper storage reservoir, can reduce pumping energy consumption by up to 166 GWh/year. Accordingly, a pumped storage hydropower facility is proposed at Paras, Pakistan, modeled, and optimized in an integrated manner with the Balakot conventional hydropower plant for a net positive annual energy generation and profitability. The integrated power and energy modeling and capacity optimization of the hydropower complex highlight the importance of suitable site selection for pumped storage hydropower near existing conventional hydropower reservoirs. Value-added contribution of this configuration is that it can enhance annual energy generation from integrated conventional hydropower, reduce the upfront capital cost of the project by 10–15%, squeeze the project implementation schedule and result in a smaller carbon footprint to meet the ultimate objective of limiting climate change. Additionally, it unlocks huge untapped pumped storage hydropower potential besides existing conventional hydropower. Furthermore, the study guides the scientific and engineering organizations on optimum capacity utilization without impeding energy generation from conventional hydropower facilities. [ABSTRACT FROM AUTHOR]

The effective pricing of retail broker in competitive electricity market constitutes a key problem toward four goals: (1) the maximization of the broker's economic benefits; (2) the balance between customers' energy supply and demand; (3) the realization of the energy supply and demand flexibility potential of customers; (4) the constraint that prevents the retail prices from too high or too low. Unfortunately, few studies can achieve four goals simultaneously. Moreover, the complicated electricity trading environment with continuous states and actions also increases the difficulty of learning optimal pricing strategy. To solve these problems, a reinforcement and imitation learning approach is proposed to develop the optimal pricing strategy of retail broker in this paper. Specifically, the proposed approach consists of a demand prediction method to predict customers' energy demand and supply volume, a self-generated expert knowledge imitation learning mechanism to instruct the agent to imitate given expert policy with generated expert knowledge, and an action policy learning method. Different from existing studies, our approach achieves all four goals and exploits the generated transition tuples fully to learn a more effective pricing strategy. The proposed scheme is verified by experiments using real-world market data, the experimental results illustrate our proposed approach gains 9.71%, 3.32%, and 15.94% higher economic profits than three state-of-the-art pricing strategies, respectively. Meanwhile, the total needed computation time for our method to learn an effectiveness pricing strategy is only 4102 s. The results show that our method gains the highest economic profits for the broker with acceptable computation cost. Moreover, the changing curves of customers' consumption/production habits demonstrate that the proposed method could achieve demand/supply response of customers. • A bi-level optimization model of retail energy market is established. • A deep reinforcement learning based pricing strategy is presented. • A self-generated imitation learning method is presented to improve learning efficiency. • Proposed method gains superior performance than three SOTA RL based methods. [ABSTRACT FROM AUTHOR]

Integrated energy systems (IESs) have attracted wide attention as effective frameworks to develop efficient use of energy resources. Proactive preparedness is a sensible approach for integrated distribution networks (IDNs) to propitiously cope with high-impact rare (HR) events. In this paper, a resilient-constrained two-stage expansion planning methodology is proposed with the aim of simultaneously optimal expansion of the IDNs and resilience improvement against hurricanes in long-term. To maintain the independence of IDN's operation and to cope with the computational complexity of such large-scale planning problem, a modified linearized alternating direction method of multipliers with parallel splitting and the adaptive penalty (LADMMPSAP) is introduced to convert the centralized model to a decentralized one. Accordingly, a new framework for the interaction between IDNs is proposed with the resilience and energy coordinator unit (RECU), which is responsible for couplings coordination between IDNs. The main goal of stage 1 is to minimize total cost, which includes investment, operation, and resilience costs. The optimal expansion scheme with optimal operation strategies is obtained in this stage. A stochastic resilience maximization under hurricane occurrence scenarios is modeled in stage 2. A normalized resilience index (RI) for evaluating resilience in IDNs suitable for long-term planning is proposed. The priorities of lines hardening and tie-line installation are determined using the proposed resilience importance index (RII) in stage 2 and sent back to stage 1. The effectiveness of the proposed methodology is tested on real-scale IDNs. The simulation results depict the privileges of incorporating resilience in long-term expansion planning from both economic and technical aspects. • A stochastic expansion planning model is proposed for integrated energy networks. • An efficient and privacy-preserving approach is used to solve the optimization model. • A resilience index is proposed to evaluate suitability of solutions in long-term planning. • A framework for the interaction among integrated distribution networks is proposed. [ABSTRACT FROM AUTHOR]

Electric and hydrogen vehicles can help decarbonize heavy duty vehicles (HDV). Few studies examine how to meet energy requirements of decarbonized HDVs, and all assume electricity will come from centralized systems. However, decarbonized HDVs could significantly increase energy demands in areas with limited transmission access, potentially favoring deployment of distributed energy resources (DERs). In this paper, we develop an optimization-based techno-economic model that minimizes costs of meeting HDV energy demands by optimizing investments in and operations of DERs, investments in transmission interconnections, and wholesale electricity purchases. We apply it to a modeled U.S. dataset of electric HDV charging demands to quantify the deployment and value potential of three DERs — solar, batteries, and nuclear small modular reactors (SMRs) in the year 2040. For fleets of 100% electric HDVs to 60% electric and 40% hydrogen HDVs, DERs are deployed at 78% to 95% of all charging stations and meet between 24% to 30% of total HDV energy demand. Investments in DERs reduce annual costs by $647 million to $1.9 billion across all stations, while individual stations can save $20 million to over $100 million annually. SMRs make up over 99% of total deployed DER capacity, indicating significant potential for SMR deployment in this emerging market. Widespread DER deployment is robust to capital cost uncertainty in SMRs and transmission lines, wholesale electricity prices, and other factors. [Display omitted] • We create a model to quantify the value of distributed energy resources for meeting decarbonized heavy duty vehicle energy demand. • Distributed energy resources are deployed at 78% to 95% of all charging stations. • Annual cost savings from distributed energy resources investments can be as high as $1.9 billion nation-wide, and $104 million at an individual station. • Small modular microreactors supply approximately between 24% and 30% of heavy duty vehicle energy demand annually. [ABSTRACT FROM AUTHOR]

• Detailed energy consumption models for different appliance categories are developed. • Modular models can be used as building blocks enhancing their flexibility. • MILP models of appliance operation can be incorporated in energy management systems. • Models allow for the integrated optimization of all energy resources. • Models enable the participation in demand response programs. Demand response programs are essential to enable accommodating larger shares of variable power generation based on renewable sources, the deployment of which is imperative for decarbonizing the economy and mitigating global warming. Consumers/prosumers are increasingly exposed to and may benefit from time-differentiated energy prices aimed to induce changes in regular consumption patterns. These changes are also beneficial for retailers and grid operators in face of the variability of wholesale market prices, renewable energy availability and grid conditions. The optimization models to be implemented in autonomous home energy management systems require a rigorous modeling of appliance operation to generate effective load scheduling solutions, respecting their physical operation principles and use patterns in everyday life. A balance should be sought between the detail level of optimization models and the computational requirements to generate usable solutions having in mind their implementation in low-cost processors. This paper presents a comprehensive and modular set of mixed-integer linear programming models aimed at enabling their seamless incorporation in home energy management systems, allowing for the integrated optimization of all energy resources (exchanges with the grid, load management, electric vehicle and stationary battery, local microgeneration). Detailed energy consumption optimization models for shiftable, interruptible and thermostatic loads are presented, also including the power cost component and ways of dealing with user's discomfort. The modular models are presented in a building block manner enhancing the flexibility of their utilization in overall models with different objective functions encompassing the economic and comfort dimensions. Computational results are presented for a case study using actual data, which considers a time-of-use tariff with six periods. In addition to comparing with a plain tariff scheme, different consumer profiles are simulated to assess the impact of comfort requirements on cost. These results show that whenever consumers have the flexibility to change their consumption patterns, they are able to lower the net electricity bill by having an energy management system endowed with the models herein proposed to make optimized decisions on their behalf. [ABSTRACT FROM AUTHOR]