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• This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency. • A demand response program integrated with home energy storage systems is presented to optimize energy consumption. • To modify the consumption pattern of household consumers, a real-time pricing algorithm is proposed. • A self-healing system reconfiguration program integrated with distributed energy resources is presented. • Real data of California households are considered to model the home appliances and home energy storage systems. This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency of the Distribution Network (DN). A Demand Response Program (DRP) integrated with Home Energy Storage Systems (HESSs) is presented to optimize the energy consumption of household consumers. Each consumer implements a Smart Home Energy Management System (SHEMS) to optimize their energy consumption according to their desired comfort and preferences. To modify the consumption pattern of household consumers, a Real-Time Pricing (RTP) algorithm is proposed to reflect the energy price of the wholesale market to the retail market and consumers. In addition, a Self-Healing System Reconfiguration (SHSR) program integrated with Distributed Energy Resources (DER), reactive power compensation equipment, and Energy Storage Systems (ESSs) is presented to manage the DN energy and restore the network loads in disruptive events. The reconfiguration operation is performed by converting the isolated part of the DN from the upstream network to several self-sufficient networked virtual microgrids without executing any switching process. Real data of California households are considered to model the home appliances and HESSs. The proposed comprehensive program is validated on the modified IEEE 123-bus feeder in normal and emergency operating conditions. [ABSTRACT FROM AUTHOR]

With the rapid expansion of computational infrastructure, the electricity consumption of geographically dispersed Internet Data Centers (IDCs) has imposed a substantial influence on the power grid. Geographically distributed IDCs, which present a versatile electricity demand with significant operational flexibility, hold potential for mitigating cost issues and balancing regional supply and load demand. To exploit this flexibility, this paper has developed a dual-drive coordinated operational strategy, which integrates locational electricity prices and unit carbon emission costs based on the dynamic carbon emission intensity (DCEI) of various regions as interaction signals to improve coordination effectiveness. The upper-level determines the consumption behaviour of IDCs, the lower-level provides feedback on dual-drive signals factoring in IDCs' involvement in grid operation, whilst an iterative algorithm is suggested to solve the bi-level model. Case studies based on real-world datasets demonstrate that the proposed operation strategy can achieve arbitrage through multi-regional carbon-electricity markets, significantly reduce operating costs, and support the rapid development of IDCs. Additionally, this strategy facilitates optimal matching of power load in IDCs with renewable energy sources, thereby mitigating imbalances in regional renewable energy supply and demand. • Analysing the necessity of integrating IDCs into the carbon market. • Proposing a dual-drive coordination to activate spatiotemporal flexibility of IDCs. • An iterative algorithm for solving the bi-level model is designed. • Comparing impacts of carbon policies in IDC costs and renewable energy accommodation. [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]

• 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]

• Household indirect energy consumption is decomposed by input-output subsystem method. • Indirect consumption is 1.3 and 1.65 times of direct in 2012 and 2017 respectively. • Coal indirect consumption accounts for 50.2%, 54.6% in 2012 and 2017 respectively. • Spillover effect is the most one among the four components. • Ferrous metal smelting and rolling sector is most affected by household activities. In recent years, China's rapid economic development and steady growth of household consumption have contributed to the continuous growth of energy consumption. In addition to considering direct household energy consumption, the indirect effects of people's consumption activities on energy consumption in other productive sectors can't be ignored. According to the input-output analysis framework and consumer lifestyle analysis, this paper mainly studies the direct and indirect consumption of energy by Chinese household consumption ways and the relationship between energy structure and lifestyle. From the perspective of structural analysis, the input-output subsystem model (scale component, own internal component, feed-back component and spillover component) is used to analyze the energy supply and demand relations among 36 sectors of the national economy related to the lives of household in 2012 and 2017, revealing the change trend and transmission mechanism. This paper also focuses on the impact of household consumption ways on energy consumption of industrial sector and changing consumer lifestyles on energy saving, meanwhile it also forecasts China's future household energy consumption under four scenarios. The results show that in 2012 and 2017, total consumption of energy which is influenced by household consumption ways in China is 28461.82 PJ and 44759.96 PJ, respectively, accounting for 26.9% and 34.1% of total consumption of final energy. The indirect energy consumption is 1.3 times and 1.65 times of direct, respectively. The coal consumption accounts for the majority of indirect energy consumption. The smelting and pressing of ferrous metals is the industrial sector which is most affected by the household consumption ways. The scale component, own internal component, feed-back component and spillover component has accounted for 18.5%, 1.3%, 0.1% and 80.0%, from 17.5%, 2.0%, 0.2% and 80.3% of energy consumption in total, respectively. Under the four scenario development modes (NN, NA, LN and LA), it is predicted that the indirect energy consumption in household life in 2025 is 43740.56PJ, 34922.93PJ, 39149.37PJ and 31257.28PJ, respectively. The implementation of low-carbon consumption mode and the accelerated decline of energy intensity will help to reduce energy consumption. [ABSTRACT FROM AUTHOR]

The energy industry is undergoing a tremendous transformation from a centralized paradigm to a decentralized one with the employment of various distributed energy technologies and information technologies. Different from the traditional urban central heating system, the district heating system (DHS) with combined energy supply by multi-energy and multi-level energy stations is considered to be promising and can improve the efficiency and flexibility of the energy system. However, its complex system structure and multiple stakeholders bring new challenges to the operation optimization. Therefore, in the face of the DHS, two optimization models are applied in this study: 1) Independent Optimization Model (IOM): the operation strategies of each energy station are optimized independently, and each station then submits its own energy demand to the upper-level energy station; 2) Collaboration Optimization Model (COM): a model established for all energy stations for global optimization. A small number of existing papers on the operation optimization of such DHS have ignored the contradiction between social benefits and operators' profits, and do not study the gap between the two models. Therefore, this paper takes COM as the benchmark, quantitatively evaluating the potential of IOM for improving the overall energy efficiency under the interest game, and grasps the key reasons that restrict its energy saving potential, aiming to improve its comprehensive benefits. An actual DHS in China was used as a case study to explore the above problems. The results show that, although COM is more energy-saving, it loses 18.3% of the profits of energy integrators, and increases the profits of energy suppliers by 13.8%. Compared with COM, the primary energy consumption of IOM increases by 19.1%. For the results, this paper finally discusses whether it is possible to further improve the energy efficiency of IOM by adjusting the heat price without impairing the profits of the operators. The results indicate that the system's primary energy consumption reduces by 5.2% without impairing the profits of each operator. • The operation strategy of the multi-energy multi-level district heating system is optimized. • The contradiction between social benefits and operator profits is considered. • Independent optimization model and collaborative optimization model are established and quantitatively compared. • An actual case study is analyzed to explore why the energy saving potential of independent optimization is restricted. • The further improvement of independent optimization is discussed. [ABSTRACT FROM AUTHOR]

The carbon trading mechanism is considered an effective method for improving energy efficiency, and reducing pollution. In this regard, two methods of considering reward-penalty on the supply and demand sides as well as user satisfaction are the most effective ways to improve the carbon trading mechanism. Herein, this paper proposes a bilayer optimization model of the community-integrated energy service system. The population in the community is mainly divided into office workers and retirees, and their preferences are fully considered. The user considers electric energy reduction and substitution modes to optimize the energy consumption behavior in the reward stage. And the service provider considers the full consumption of wind and photovoltaic power and optimizes the output of each unit in the penalty stage. The effect of reward-penalty factors on the supply and demand sides is analyzed. The algorithm used to achieve bilayer optimization is the hybrid solution algorithm, which adopts the improved particle swarm optimization algorithm and CPLEX solver. The experimental results show that the costs of office workers and retirees are reduced by an average of 18.2% and 12.2%, respectively. And the service provider's profit is increased by 15.28%. The results also provide site selection strategies for energy supply. • A carbon trading mechanism that considers the reward-penalty is proposed. • An integrated energy service system that considers user satisfaction is proposed. • The total cost of users has dropped by an average of 12.72%. • The profit of the energy service provider has increased by an average of 15.28%. • The conclusions provide certain help of site location for energy supply. [ABSTRACT FROM AUTHOR]

Abstract: The implementation of the European Directive on the Energy Performance of Buildings (EPBD) is a milestone towards the improvement of energy efficiency in the building sector. However, even in cases where impressive measures can be implemented in the densely built urban environment, the less glamorous measure of building''s envelope thermal insulation remains a prerequisite towards the improvement of the building''s energy efficiency. Despite the insulation requirements specified by national regulations, thermal bridges in the building''s envelope remain a weak spot in the constructions. Moreover, in many countries construction practices tend to implement only partially the insulation measures foreseen by regulations. As a result, thermal losses are in practice greater than those predicted during the design stage. This paper presents a study on representative wall thermal insulation configurations used in Greek buildings, in order to investigate the impact of the thermal bridges on the energy consumption. The double wall construction, used widely in Greece and not only there, is rather susceptible to the occurrence of thermal bridges, in contrast to a typical thermal insulating façade, like the one applied in Central Europe. The analysis of the thermal bridges’ impact will in that sense also highlight the potential for energy renovation measures in older buildings. [Copyright &y& Elsevier]

Abstract: This paper proposes an energy consumption change forecasting system using fuzzy logic to reduce the uncertainty, inconvenience and inefficiency resulting from variations in the production factors. The proposed fuzzy logic approach helps the manufacturer forecast the energy consumption change in the plant when certain production input factors are varied. Predictions given by the proposed system adopts the fuzzy rule reasoning mechanism so that any changes in the overall energy consumption will neither violate the stable power supply and production schedules nor result in energy wastage. To demonstrate how the fuzzy logic approach is applied to a manufacturing system, a case study of the energy consumption forecast in a clothing manufacturing plant has been conducted in an emulated environment. The result of the case indicates a percentage change in the plant’s energy consumption after analyzing three input parameters. This finding is able to provide a solid foundation on which decision makers and systems analysts can base suitable strategies for ensuring the efficiency and stability of a manufacturing system. [Copyright &y& Elsevier]

Abstract: Many factors influence the energy consumption of buildings, particularly those related to their design and use. Other factors, such as orientation and the nature of the surroundings, also have an influence, but these are often ignored. In this paper, we consider the influence of trees planted as a shelterbelt, using computer simulations to model the varying energy consumption of a building with or without the shelterbelt trees. It is concluded that a reduction of up to 18% in heating energy consumption can be made by planting an optimum shelterbelt of trees in appropriate situations. [Copyright &y& Elsevier]

Abstract: Energy-aware compilers are becoming increasingly important for embedded systems due to the need to meet a variety of design constraints on time, code size and power consumption. This paper introduces for the first time a trace-based, link-time compiler framework on binaries for embedded systems and evaluates its potential benefits in supporting energy optimisations, especially those that exploit the interaction between compilers and architecture. We present two algorithms for reducing leakage energy in functional units and data caches, respectively. Both algorithms work uniformly at the granularity of optimisation regions that are formed by the hot traces of a program. Our experimental results using Mediabench benchmarks show that good leakage energy savings can be achieved at the cost of some small performance and code size penalties. Furthermore, by varying the granularity of optimisation regions, which is a tunable parameter, embedded application programmers can make the tradeoffs between energy savings and these associated costs. [Copyright &y& Elsevier]