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In modern power system applications, security constrained optimal power flow (SCOPF), in which different contingency operating situations arise, is a complex, non-convex, and nonlinear optimization problem. With the inclusion of stochastic solar and wind energy sources in the power system, the energy efficiency is maximized. However, this increases the complexity of the SCOPF problem and makes its solution difficult. To tackle these challenges, it's essential to design an algorithm with a search behavior that aligns with the characteristics of the OPF/SCOPF problem. For this reason, in this paper, a novel dynamic fitness-distance balance based growth optimizer (dFDB-GO) algorithm was proposed to find the optimal solution of the OPF/SCOPF problem. By using the dFDB method, the guide selection in the learning stage of the GO algorithm was redesigned to improve the search capability and to provide a good balance between exploration and exploitation. To prove the performance of the dFDB-GO algorithm, the simulation study was performed on the SCOPF and benchmark problems. In the simulation study carried out on the optimization of benchmark problems using the six variations of GO and the base GO algorithms, all variations outperformed the base GO algorithm according to Friedman test results. In other simulation study, the performance of the proposed algorithm was tested on OPF/SCOPF problem including wind and solar energy sources. One of the important contribution of this study is to present a comprehensive simulation study to the literature, including twelve case studies using modified IEEE 30-bus and 57-bus systems and nine different objective functions. These case studies were solved by the dFDB-GO and eleven up-to-date MHS algorithms. The another important contribution was that a comprehensive analysis was carried out using the minimum, maximum, mean, and standard deviation values of the algorithms and the statistical analysis methods. Accordingly, the proposed dFDB-GO algorithm achieved better results than its rival in 11 of 12 case studies. Moreover, it ranked first with 1.3077 value among its competitors according to Friedman test. On the other hand, the stability of an algorithm on solving SCOPF problem was evaluated for the first time in this study. For this, while the dFDB-GO algorithm achieved 94.87% mean success rate for solving the SCOPF problem, the GO algorithm had the 12.09% mean success rate value. To sum up, all analysis results prove the superior performance of the proposed algorithm in solving the SCOPF problem against to its rivals. [ABSTRACT FROM AUTHOR]

The electric power distribution system (PDS) and the water distribution system (WDS) are coupled with each other through electricity-driven water facilities (EdWFs), such as pumps, water desalination plants, and wastewater treatment facilities. However, they are generally owned and operated by different utilities, and there does not exist an operator that possesses full information of both systems. As a result, centralized methods are not applicable for coordinating the operation of the two systems. This paper proposes a decentralized framework where the PDS and WDS operators solve their own operation problems, respectively, by sharing only limited information. Nevertheless, the boundary variables (i.e., the variables shared between two systems) are discontinuous due to their dependence on the on/off nature of EdWFs. Unfortunately, mature decentralized/distributed optimization algorithms like the alternating direction method of multipliers (ADMM) cannot guarantee convergence and optimality for a case like this. Therefore, this paper develops a novel algorithm that can guarantee convergence and optimality for the decentralized optimization of PDS and WDS based on a recently developed algorithm called the SD-GS-AL method. The SD-GS-AL method is a combination of the simplicial decomposition (SD), gauss–seidel (GS), and augmented Lagrangian (AL) methods, which can guarantee convergence and optimality for mixed-integer programs (MIPs) with continuous boundary variables. Nonetheless, the original SD-GS-AL algorithm does not work for the PDS-WDS coordination problem where the boundary variables are discontinuous. This paper modifies and improves the original SD-GS-AL algorithm by introducing update rules to discontinuous boundary variables (called the Auxiliary Variables Update step). The proposed mixed-integer boundary compatible (MIBC) SD-GS-AL algorithm has the following benefits: (1) it is capable of handling cases whose boundary variables are discontinuous with convergence and optimality guaranteed for mild assumptions, and (2) it only requires limited information exchange between PDS and WDS operators, which will help preserve the privacy of the two utilities and reduce the investment in building additional communication channels. Simulations on two coupled PDS and WDS test cases (Case 1: IEEE-13 node PDS and 11-node WDS, and Case 2: IEEE-37 node PDS and 36-node WDS) show that the proposed MIBC algorithm converges to the optimal solutions while the original SD-GS-AL does not converge for both test cases. The ADMM does not converge for the first test case while it converges to a sub-optimal solution, 63 % more than the optimal solution for the second test case. • We propose a decentralized optimization of the water-energy nexus. • The proposed algorithm handles mixed-integer boundary variables. • The optimality and convergence of the proposed algorithm are guaranteed. • Outperforms mature distributed algorithms like ADMM. [ABSTRACT FROM AUTHOR]

The sustainable energies take increasing proportion in the power systems due to the "net-zero emission" goal, and the future trend is to make the new type power systems operate safely and stably while maintaining low carbon and economic efficiency. This paper proposes a novel smart generation system (SGS) architecture and a smart generation system consensus (SGSC) algorithm from the perspective of automatic generation control. The SGS is based on stratified sequencing method and multi-agent theory, which describes the dynamic and optimal generation dispatch of the units as a hierarchical decentralized multiple objectives programming problem. Further, the SGSC is proposed based on distributed Newton algorithm and leaderless consensus algorithm to solve the security performance degradation caused by decentralization. All the units are configured in parallel with the designed communication topology to achieve dispatch decentralization, thus ensuring low-carbon, economical and safe operation of the new type power system. The effectiveness of the proposed algorithm is verified on a multi-layer, multi-zone smart generation system model with a high proportion of sustainable energies from macro and micro aspects. Different control methods are also used for performance comparison on the two-area load frequency control model with lower regulation costs and lower carbon emissions achievement. • A decentralized control Architecture of autonomous SGS is developed. • A novel SGSC algorithm is proposed for dynamic generation command optimization of AGC. • The proposed algorithm not only minimizes regulation costs and carbon emissions but also ensures the stability of system operations, even during communication failures. [ABSTRACT FROM AUTHOR]

• The AES power system intelligent energy scheduling environment is built. • The BI-LSTM-Att action network is propose by combining BI-LSTM with attention mechanism. • The DQN-CE algorithm is proposed by improving the DQN algorithm. • The experiments on an all-electric ferry is conducted using a variety of methods. At present, Ship energy scheduling based on deep reinforcement learning (DRL) is an important research direction in which Deep Q learning algorithm (DQN) has been successfully applied in the field of energy scheduling. In terms of the shortcomings of DQN in all electric ships(AES) energy scheduling, such that, the insufficient performance of using multiplayer perceptron(MLP) as action network and the degradation of convergence performance due to over estimation of Q value, in this paper, we propose a DQN cross entropy(DQN-CE) energy scheduling algorithm combing bi-directional LSTM and attention mechanism(BI-LSTM-Att). Instead of using MLP, firstly, we use BI-LSTM-Att as action network of DQN, and then an improved DRL algorithm called DQN-CE is proposed, that is, we add cross-entropy loss of predicted action of the next time for the target network and the action network to DQN. Finally, we conduct simulation experiments on an all-electric ferry, and the experimental results show that the energy scheduling algorithm proposed in this paper reduces the economic consumption by 4.11%, increases the utilization rate of energy storage system by 24.4%, and reduces the exploration time of agent training by 31.3% compared with the original DQN energy scheduling algorithm. Finally, the effectiveness and superiority of the algorithm were further verified in the new case study. [ABSTRACT FROM AUTHOR]

Traditional virtual power plants (VPPs) with fixed resource composition and coordination strategies struggle to cost-effectively exploit the flexibility of large-scale resources for adapting variable regulation requirements and resources characteristics. To this end, this paper proposes a dynamic aggregation mechanism to flexibly select and coordinate individual resources for forming aggregators according to grids regulation requirements and resource characteristics. The proposed mechanism is operated through a two-stage dynamic aggregation model comprising resource selection and coordination. Considering the two-stage dynamic aggregation model is a combinational optimization problem with high computational complexity, the submodular optimization method is utilized to swiftly address this problem. First, the complementarity and submodularity of the dynamic aggregation process are formulated to elaborate how the aggregation regulation characteristics (ARCs) evolve with flexible resource composition and coordination. Next, a submodularity-based algorithm is developed to promptly solve dynamic aggregation model under three scenarios, where aggregation operators focus on the resources quantity, quality, and cost-effectiveness, respectively. The polynomial computational complexity of the proposed algorithm has also been evaluated. Simulations using the IEEE 39-bus (New England) system consists of 10,000 flexible resources were executed to assess the submodularity approach. The proposed algorithm demonstrates superior computing speed and better performance guaranteed results (90%, 97%, 90% in three scenarios) compared to other methods—making it more suitable for implementation in practice. • The complementarity and submodularity of dynamic aggregation process are revealed and proved. • A two-stage dynamic aggregation model consists of resources selection and coordination is established. • A dynamic aggregation algorithm is proposed to deal with different aggregation scenarios. • The approximate guarantees and polynomial computational complexity of algorithm are proved. [ABSTRACT FROM AUTHOR]

Accurately capturing data on the external loads that large structural systems endure is crucial for improving the performance of energy equipment. This paper introduces a novel hybrid model-data-driven framework for the dynamic load identification of interval structures, which seamlessly combines finite-element modeling with machine learning techniques. To address potential ill-posed issues in model-driven methods and the interpretability limitations of data-driven methods, we propose a physics-informed neural network. This neural network effectively inverts uncertain modal responses with low data requirements and high predictive performance high by integrating the underlying modal transformation equation into the loss function of a fully connected neural network. To identify the modal loads using predicted modal displacement/acceleration responses, we introduce a pioneering dynamics inversion method. This method modifies the traditional Kalman filter with an assumption of unknown inputs to reduce the sensitivity of load identification process to different noises. In addition, our approach incorporates a subinterval Chebyshev expansion method to adaptively determine the interval boundaries of external loads. The efficiency of the proposed method is assessed through two numerical examples and validated through comparative research against baseline methods. Our findings suggest that this approach enhances precision, robustness, and generalization in dynamic load identification, even when facing challenges such as limited training data, significant noise interference, and non-zero initial conditions. • A novel hybrid model-data-driven dynamic load identification method for interval structures is proposed. • A physics-informed neural network is established to inverse the uncertain modal responses effectively. • Two improved Kalman filter algorithms are developed to identify the modal loads at specified parameters. [ABSTRACT FROM AUTHOR]

This work investigates the design optimization of aggregated energy systems (multi-energy systems, microgrids, energy districts, etc.) with (N-1) -reliability requirements. The problem is formulated as a two-stage stochastic Mixed Integer Linear Program which optimizes design (first stage variables) and operation variables (second stage variables) simultaneously considering a set of typical and extreme days. The analysis proposes and compares different approaches to include the (N-1) reliability requirement in the optimization problem. Moreover, the paper proposes two effective decomposition algorithms to solve the large-scale Mixed Integer Linear Program suitable for design problems with and without (N-1) reliability requirements. Depending on the instance, such decomposition algorithms allow reducing the computational time by one or more orders of magnitude (from days to a few hours, in the worst cases tested in this work). The proposed methodology is tested to design the aggregated energy system for a real case study considering both a grid-connected and off-grid installation. Results indicate that the actual reliability of the design solutions depends by the profiles of energy demand and renewable production considered in the failure scenarios included in the design problem. Including N-1 reliability requirements causes an increase in the total annual cost in the range 15–20%, due to the increase in capital costs. • N-1 reliability is included in MILP for the optimal design of Aggregated Energy Systems. • Different approaches to include reliability constraints are proposed and compared. • Two decomposition algorithms allow reducing the computational time considerably. • Including N-1 reliability results in a 15–20% Total Annual Cost increase. [ABSTRACT FROM AUTHOR]

Highlights • An automated model estimation and selection algorithm is proposed. • The search algorithm utilizes a forward-looking model validation process. • This paper proposes a fair testing procedure for predictive analytics. • The proposed expert system does not require any prior theory or assumption. • This architecture re-estimates and re-searches the model with new data. Abstract This paper proposes an intelligent model search engine (IMSE), an integrated model selection algorithm, subject to the out of sample predictive performance and given set of explanatory variables for forecasting crude oil prices. In the conventional applications of energy price forecasting, models are selected based on preliminary assumptions on causality and model structure (e.g. lag length in lagged variables). Relaxation of those assumptions would cause over-fitting and reduce the degree of freedom. Considering the ultimate objective of forecasting models, any variations of models may be tested in the out-of-sample period, and the optimization problem can be redefined as minimization of post-sample error metric in a validation set. By this, data mining would be a legitimate operation for economic forecasting, and it also proves required conditions usually tested by diagnostic tests such as Akaike Information Criterion for model quality. IMSE is a multi-input/single output difference equation based approach which allows users to test various models (for given set of explanatory variables) as well as various order of lagged inputs (lag length) without a priori assumption or theoretical basis except defining set of potential inputs. Finally, it selects the best model subject to predictive accuracy in a validation set. Empirical results indicated that the proposed algorithm significantly outperformed a broad range of benchmark methodologies as well as proving that certain assumptions of econometric approach (e.g. statistical significance of explanatory variables) are independent of predictive performance. [ABSTRACT FROM AUTHOR]

Energy storage systems have been recently recognized as an effective solution to tackle power imbalances and voltage violations faced by distribution system operators due to the increasing penetration of low carbon technologies. To fully exploit their benefits, optimal sizing of these devices is a key problem at the planning stage. This paper considers the sizing problem of the energy storage systems installed in a distribution network with the aim, e.g., of preventing over- and undervoltages. In order to accommodate uncertainty on future realizations of demand and generation, the optimal sizing problem is formulated in a two-stage stochastic framework, where the first stage decision involves the storage sizes, while the second stage problem provides the optimal storage control policy for given demand and generation profiles. By taking a scenario-based approach, the two-stage problem is approximated in the form of a single multi-scenario, multi-period optimal power flow, whose size, however, becomes computationally intractable as the number of scenarios grows. To overcome this issue, the paper presents a procedure to compute upper- and lower bounds to the optimal cost of the approximate problem. Moreover, when the objective is to minimize the total installed storage capacity, an iterative algorithm based on scenario reduction is proposed, which converges to the optimal solution of the approximate problem. The whole procedure is tested on the topology of the IEEE 37-bus test network, considering scenarios of demand and generation which feature over- and undervoltages in the absence of storage devices. [ABSTRACT FROM AUTHOR]

Along with the technical feasibility of microgrids, the importance of supervisory communications, computing, and control (CCC) systems that are necessary to operate a microgrid in a stable and economically feasible manner have been firmly established in the literature. While several investigators have proposed and demonstrated algorithms and applications of CCC, the functional entities and responsibility centers for realizing CCC within microgrids in the context of multiple independent customers and distribution system entities have not been situated and/or studied. In this context, the term emergent microgrid may be used to define an electrical network that consists of local generation, loads, and storage. Such a grid has the potential to be clustered as a microgrid, but does not have all the CCC features to become a full-fledged microgrid. In this paper, a simple electric utility platform (SEUP) that is an end-to-end solution with all the hardware/software interfaces and components necessary for augmenting emergent microgrids with CCC into full-fledged microgrids is presented. SEUP abstracts the details of sensing and CCC for the microgrid developers, installers, operators and end-users to provide a seamless system. SEUP is distinctive from other CCC platforms in that it is entirely based on publicly available infrastructure resources (wireless cellular networks, internet and cloud computing) without any intellectual property restrictions. The paper introduces the functional roles, main architecture, components and security features of SEUP along with the results from a sample application case of a laboratory-scale power system. [ABSTRACT FROM AUTHOR]

As for the internal load of electricity users, it can be determined by non-intrusive load monitoring (NILM), which is conducive to peak-load shift in the power grid. At present, the research on NILM focuses mainly on residential appliances, while industrial loads that consume large amounts of electricity may have more effective regulation capabilities in the face of large fluctuations in grid power brought about by a great deal of new energy connected to the grid, however, there is still little research conducted on the non-intrusive monitoring of industrial loads. In this paper, our consideration is given to the fact that industrial loads, compared to residential loads, operate steadily in the same state for a long time. Then, a non-intrusive industrial load disaggregation method is proposed on the basis of load operating states and improved grey wolf algorithm. On the one hand, this method takes into account the different times of load operation in different states, with the expanded label method proposed to improve the initialization of the population. On the other hand, median filtering is performed to process the results of first-generation load disaggregation in two times. Moreover, a strategy is proposed for multiple disaggregation of a single power according to this method. In addition, the search mechanism of the grey wolf algorithm is improved through the beta distribution of the three-point estimate and the genetic algorithm. According to the experimental results, the proposed method is effective in improving the accuracy of load disaggregation, the impact of high-power load on low power load disaggregation is mitigated to a certain extent, and the accuracy of each load reaches a minimum of 90%. • Optimize the initialization with the operating time of each state of the load. • Process the disaggregation values with the double median filtering. • Disaggregate a single power multiple times, with the median of disaggregation results as the initial disaggregation result. • Mitigate the impact of high-power loads on the disaggregation of low-power loads. [ABSTRACT FROM AUTHOR]

To further improve the power generation efficiency of traditional photovoltaic (PV) systems, this paper designs a theoretical model of a hybrid power generation system that consists of the individual PV system and thermoelectric generation (TEG) system. Meanwhile, partial shielding condition (PSC) is a common but serious problem during operation that might lead to power loss and component mismatch in hybrid PV-TEG system. Therefore, a reconfiguration method for the hybrid PV-TEG system based on artificial rabbit optimization (ARO) algorithm is proposed in this study to alleviate the negative impact caused by PSC and thus improve the power generation efficiency of the hybrid system. ARO algorithm is applied to adjust the switching matrix of the hybrid system to change the electrical connection among PV arrays and TEG arrays, and thus further to reduce the adverse effect of PSC and maximize the output power of the hybrid system. To verify the effectiveness of the proposed method, simulation tests are carried out on 4 × 4 and 20 × 15 arrays, respectively. For a quantitative and fair comparison, this work employs maximum output power, average output power, mismatch loss, and standard deviation as evaluation indexes, upon which four different algorithms including GA, PSO, WOA, AOA and ACO are thoroughly compared. Simulation results show that the output power of the hybrid system after ARO algorithm based reconfiguration is improved by 34.05% in the 4 × 4 array and 23.10% in the 20 × 15 array, respectively. In addition, hardware-in-the-loop (HIL) experiments are carried out based on RTLAB platform to verify the hardware feasibility of the proposed reconfiguration strategy. • A hybrid PV-TEG system model is introduced and constructed in this work; • A modular reconfiguration scheme for hybrid PV-TEG systems is proposed, in which the PV and TEG array components are modularized to reduce the overall costs; • This work devises a reconfiguration method for hybrid systems based on ARO algorithm; • The HIL experiment based on RTLAB platform is undertaken to further prove the implementation feasibility of the reconfiguration strategy. [ABSTRACT FROM AUTHOR]

The "shoeboxing" algorithm is a simplification approach capable of converting a building of any shape into a representative shoebox, with the aim of speeding-up Urban Building Energy Modeling simulations. The procedure works towards accurately predicting both annual energy needs and hourly thermal loads, going beyond the preliminary assessment of buildings' thermal performance at city-scale. Furthermore, the simplification has been particularly developed to work with buildings of complex geometry considering adjacencies and obstructions. After a first validation performed for stand-alone buildings, in this paper, the capabilities of the algorithm are evaluated at district-level on fictional parametrically generated layouts and in different climatic conditions. As a whole, the shoeboxing algorithm properly predicted both heating and cooling needs at building-level in all the considered climatic conditions, yielding annual differences within ±10% and ± 20%, respectively for cooling and heating. Moreover, both annual and hourly deviations showed to be similar in the different climates considered, suggesting that the simplification can be reliably employed worldwide. Finally, the thermal simulation time has been reduced up to 36 times. • District-scale validation of an algorithm for Urban Building Energy Modeling • Buildings are converted in shoeboxes including adjacencies and urban context • The simplification is validated on 64 districts of 324 buildings in three climates • Heating and cooling needs are predicted with differences within ±20% and ± 10% • The algorithm reduced the thermal simulation time up to 36 times [ABSTRACT FROM AUTHOR]

Highlights • Co-simulation and building energy models provide a framework to test control algorithms. • Machine learning algorithms are effective to develop predictive optimisation models. • Predictive controls for residential can reduce heating systems costs up to 40%. • Smart predictive control in buildings results in lower carbon footprint up to 39%. • The smart grid dynamically controls dwellings to balance electricity supply/demand. Abstract This paper assesses the performance of control algorithms for the implementation of demand response strategies in the residential sector. A typical house, representing the most common building category in Ireland, was fully instrumented and utilised as a test-bed. A calibrated building simulation model was developed and used to assess the effectiveness of demand response strategies under different time-of-use electricity tariffs in conjunction with zone thermal control. Two demand response algorithms, one based on a rule-based approach, the other based on a predictive-based (machine learning) approach, were deployed for control of an integrated heat pump and thermal storage system. The two algorithms were evaluated using a common demand response price scheme. Compared to a baseline reference scenario, the following reductions were observed: electricity end-use expenditure (20.5% rule-based and 41.8% predictive algorithm), utility generation cost (18.8% rule-based and 39% predictive algorithm), carbon emissions (20.8% rule-based and 37.9% predictive algorithm). [ABSTRACT FROM AUTHOR]

Highlights • Auction-theory-based method is proposed for calculating coalition utility • Coalition formation techniques are introduced to find stable coalition partition • Economy benefits of microgrids cooperation are simulated and analyzed Abstract The future distribution network may encompass a large number of microgrids, in which case local networked microgrids can be formed and all the microgrids in the network be connected to the main grid through a distribution sub-station. To improve the efficiency of the entire network rather than focusing on improving the efficiency and reliability of each microgrid, this paper proposes a coalitional-game-theory-based local power exchange algorithm to identify incentives for coalitional operation and help microgrids in the network trade power locally with neighboring microgrids, so as to meet their own power requirements while achieving higher expected individual utility. Compared with the traditional operation situation, simulation results show that the proposed coalitional- game-theory-based local power exchange algorithm can help increase individual microgrid utility in the network. When there are 30 microgrids in the network, for example, each microgrid is expected to have a 16% increment of individual utility on average. [ABSTRACT FROM AUTHOR]

Highlights • Improve nearly zero energy building performances at community level by collaborations. • Develop a grouping method that can maximize the collaboration benefits. • Identify representative energy characteristics by clustering algorithm. • Study performance improvements in economic cost and grid interactions. Abstract Collaborations among nearly zero energy buildings (nZEBs) (e.g. renewable energy sharing) can improve nZEBs' performance at the community level. To enable such collaborations, the nZEBs need to be properly grouped. Grouping nZEBs with similar energy characteristics merely brings limited benefits due to limited collaboration existed, while grouping nZEBs with diverse energy characteristics can bring more benefits. In the planning of nZEB communities, due to the large diversity of energy characteristics and computation complexity, proper grouping that maximizes the collaboration benefits is difficult, and such a grouping method is still lacking. Therefore, this paper proposes a clustering based grouping method to improve nZEB performance. Using the field data, the grouping method first identifies the representative energy characteristics by advanced clustering algorithms. Then, it searches the optimal grouping alternative of these representative profiles that has the optimal performance. For validation, the proposed grouping method is compared with two cases (the nZEBs are either not grouped or randomly grouped) in aspects of economic costs and grid interaction. The study results demonstrate that the proposed method can effectively improve nZEBs' performances at the community level. The propose method can provide the decision makers a means to group nZEBs, which maximize the collaboration benefits and thus assists the planning of nZEB communities. [ABSTRACT FROM AUTHOR]

Highlights • Decomposition-based hybrid wind energy forecasting models are reviewed. • Techniques improving the performance of decomposition-based models are discussed. • Challenges in the application of decomposition-based models are discussed. • Potential directions improving the accuracy of these models are discussed. Abstract With the continuous growth of wind power integration into the electrical grid, accurate wind power forecasting is an important component in management and operation of power systems. Given the challenging nature of wind power forecasting, various methods are presented in the literature to improve wind power forecasting accuracy. Among them, combining different techniques to construct hybrid models has been frequently reported in the literature. Decomposition-based models are a family of hybrid models that firstly decompose the wind speed/power time series into relatively more stationary subseries, and then build forecasting models for each subseries. In this paper, we present a comprehensive review of decomposition-based wind forecasting methods in order to explore their effectiveness. Decomposition-based hybrid forecasting models are classified into different groups based on the decomposition methods, such as, wavelet, empirical mode decomposition, seasonal adjust methods, variational mode decomposition, intrinsic time-scale decomposition, and bernaola galvan algorithm. We discuss decomposition methods in the context of alternative forecasting algorithms, and explore the challenges of each method. Comparative analysis of various decomposition-based models is also provided. We also explore current research activities and challenges, and identify potential directions for future research on this subject. [ABSTRACT FROM AUTHOR]

Highlights • Hourly-resolved model to investigate highly-renewable electricity generation in Spain. • Correlation analysis of time series to create 900 combinations used in simulations. • Transition paths evaluated based on security of supply, CO 2 emissions, and renewable share. • Short-term phase-out of nuclear and coal power plants proven to be feasible. Abstract Two alternative paths to achieve highly-renewable electricity generation in peninsular Spain are investigated in this paper. Every transition path comprises a description of the installed and decommissioned generation and storage capacities, from 2017 to 2030, as well as a hypothesis on the evolution of the electricity demand. The electricity mix for every hour within the transition path is determined through a dispatch algorithm that prioritizes electricity from renewable energy sources. The simulation is run for 900 different combinations of time series representing the hourly capacity factors of different technologies, as well as the electricity demand. This robust approach allows the evaluation of the transition paths based on the statistical distribution of several defined assessment criteria, such as security of supply, CO 2 emissions or renewable share in electricity generation. The feasibility of a Spanish power system with high renewable penetration is investigated not only in a future reference year but throughout the transition path. In particular, a progressive and simultaneous phase-out of nuclear and coal power plants in the short-term is proven to be feasible. Furthermore, the results sensitivity is analyzed including scenarios with a delayed nuclear phase-out, lower hydroelectricity generation due to more frequent and severe droughts caused by climate change and higher annual increment for the electricity demand. [ABSTRACT FROM AUTHOR]

Highlights • Create city-specific energy performance grading model using city disclosure data. • Method to grade buildings that is interpretable, reproducible, scalable, and robust. • Develop a clustering algorithm to grade buildings on performance. • Validated approach demonstrates significant improvements over current methods. Abstract As the effects of anthropogenic climate change become more pronounced, local and federal governments are turning towards more aggressive policies to reduce energy use in existing buildings, a major global contributor of carbon emissions. Recently, several cities have enacted laws mandating owners of large buildings to publicly display an energy efficiency rating for their properties. While such transparency is necessary for market-driven energy reduction policies, the reliance on public-facing energy efficiency grades raises non-trivial questions about the robustness and reliability of methods used to measure and benchmark the energy performance of existing buildings. In this paper, we develop a building energy performance grading methodology using machine learning and city-specific energy use and building data. Leveraging the growing availability of data from city energy disclosure ordinances, we develop the GREEN grading system: a framework to facilitate more accurate, fair, and contextualized building energy benchmarks that account for variations in the expected and actual performance of individual buildings. When applied to approximately 7500 residential properties in New York City, our approach accounts for the differential impact of design, occupancy, use, and systems on energy performance, out-performing existing state-of-the-art methods. Our model and findings reinforce the need for more robust, localized approaches to building energy performance grading that can serve as the basis for data-driven urban energy efficiency and carbon reeduction policies. [ABSTRACT FROM AUTHOR]

Highlights • A novel linearized multi-objective optimal energy flow of IEGS is constructed. • A reliable convergence is ensured with an incremental piecewise linearization. • A novel HAPM for complex optimization with conflicting objectives is proposed. • HAPM surpasses existing methods to get a full distribution of Pareto solution set. Abstract This paper constructs a novel multi-objective optimal energy flow of an integrated electricity and gas system to fully exploit complementary benefits of the system. To ensure a reliable convergence, an incremental piecewise linearization is employed to linearize the original nonlinear problem into a mixed integer linear programming. Actually, the proposed issue represents a highly constrained optimization with numerous variables and multiple conflicting objectives. To effectively solve the problem, a novel analytical Pareto optimizer called homogenized adjacent points method (HAPM) is first proposed, which aims to obtain a Pareto solution set with three basic strategies, including axes homogenization, adjacent points calculation and adjacent points filtration. Compared to the existing methods, the major superiority of HAPM is that the algorithm can obtain a full distribution of Pareto solution set with the boundary of Pareto front and non-dominated quality of the solutions. Finally, a best compromise solution is identified from the Pareto solution set using an entropy weight based ideal point method. Simulation results indicate an environmentally friendly and reliable integrated electricity and gas system is realized with a slight cost sacrifice, and validate the robustness of HAPM to obtain a non-dominated, uniform and widespread Pareto solution set compared with existing methods. [ABSTRACT FROM AUTHOR]

Highlights • Microgrids can be a valid alternative to grid extension for rural electrification. • Advance dispatch strategies and proper system design can significantly reduce costs. • We propose a new catalog-based microgrid design optimization algorithm. • Yearly performance is assessed via a realistic Rolling Horizon simulation. • The algorithm is applied to optimal regional electrification planning. Abstract Off-grid microgrids constitute an increasingly viable alternative to grid extension for rural electrification. Thanks to significant reductions in hardware cost, energy cost for off-grid users in areas sparsely covered by the national grid has become competitive with grid-connection once infrastructural costs are accounted for. Microgrids offer advantages also from an environmental and social point of view, featuring generation systems with high renewable penetration and facilitating the involvement of local communities in development projects. To further enhance microgrids economic competitiveness, it is necessary to devise innovative control strategies and design algorithms, that can ensure an optimal operating performance at the lowest overall cost. In this paper we present a new predictive design and dispatch optimization algorithm based on Mixed Integer Linear Programming (MILP). The new method is compared to a previously developed heuristic methodology, applying both to the design and yearly performance estimation of local microgrids, characterized on the basis of real data relative to a sub-Saharan African region. The potential advantages of including the proposed microgrid design method in a regional electrification planning model are illustrated in a realistic case example. [ABSTRACT FROM AUTHOR]

Highlights • A combined model is proposed for multi-step ahead wind speed forecasting. • Data preprocessing technology is introduced to improve the forecasting performance. • Quasi-Newton algorithm is used to increase the particle diversity of water cycle algorithm. • The accuracy and stability of wind speed forecasting are improved simultaneously. • The simulation results are validated well in China. Abstract Owing to the complexity and uncertainty of wind speed, accurate wind speed prediction has become a highly anticipated and challenging problem in recent years. Researchers have conducted numerous studies on wind speed prediction theory and practice; however, research on multi-step wind speed prediction remains scarce, which hinders further development in this area. To improve upon the accuracy and stability of multi-step wind speed prediction, this paper proposes a combination model based on a data preprocessing strategy, an improved optimization model, a no negative constraint theory, and several single prediction models. To improve upon forecasting performance, an improved water cycle algorithm based on a quasi-Newton algorithm is proposed to optimize the weight coefficients of the single models. In the empirical research, 10-min and 30-min wind speed data from Shandong Province in China, collected for case studies, were used to assess the comprehensive performance of the proposed combination model. Finally, we used 10-fold cross-validation and multiple error criteria to evaluate the comprehensive performance of the proposed combination model. The simulation results indicate that (a) the quasi-Newton algorithm can effectively increase the diversity of the water cycle algorithm particles, resulting in improved water cycle algorithm optimization performance; (b) the combination model exhibits superior predictive performance to a single model by taking advantage of each single model; and (c) the proposed combination model can effectively improve multi-step wind speed prediction results. [ABSTRACT FROM AUTHOR]

Highlights • A distributed network loss optimization approach is present to increase energy utilization. • A fully distributed voltage control method is proposed. • A topology simplification method with DPMU data at controllable DGs/loads is proposed. • The coordinated control system is based on incomplete measurements. Abstract This paper explores a distributed coordinated control algorithm for active distribution networks (ADNs) based on measurements of distribution phasor measurement units (DPMUs) that are only installed at a portion of network nodes. First of all, a network topology simplification method is proposed to deal with the lack of impedance and power information in ADNs. Specifically, sub-networks with unknown impedance and power information are transformed to simplified networks with known equivalent parameters that are derived from the measurements of DPMUs at the sub-network boundaries. After that, a fully distributed control strategy utilizing a multiple agent system (MAS) is employed to control voltage during voltage excursions in ADNs, and to optimize network losses if no voltage excursions occur. MAS achieves global voltage control through distributed decision-making according to the voltage-power sensitivities that are calculated by each agent based on local and neighbourhood measurements. MAS balances reactive power locally to reduce network active power losses. The convergence of the proposed distributed voltage control method is proved. Finally, the feasibility and effectiveness of the proposed method has been demonstrated on a modified IEEE 33-bus system. [ABSTRACT FROM AUTHOR]

Highlights • Capacity credit (CC) of demand response (DR) under the smart-grid is assessed. • Proposed a hybrid probabilistic-possibilistic framework for DR modeling. • Both exogenous and endogenous uncertainties associated with DR are considered. • A reliability-based algorithm combining operation optimization is developed. • The impacts of various factors on DR CC are analyzed through numerical studies. Abstract As a featured smart-grid technology, demand response (DR) provides utility companies with unprecedented flexibility to improve the reliability of electricity service in future power systems. However, due to the uncertainties arising from the demand side, the extent to which DR can be utilized for capacity support poses a major question to the utilities. To address this issue, this paper proposes a new methodological framework to assess the potential reliability value of DR in smart grids. The framework is established on the concept of capacity credit (CC), and it accommodates different types of uncertainties (i.e., probabilistic and possibilistic) accrued from physical and anthropogenic factors in DR programs. The capability of DR during operation is considered as a synthesized result of multiple facets, i.e., users' load characteristics, participation levels, and load recoveries, and different models are developed to represent each component. To characterize the stochastic nature of demand responsiveness, the fuzzy theory is introduced, and possibilistic models are proposed to describe the human-related uncertainties under incomplete information. In addition, considering that in reality, DR operation could affect the comfort of customers, the dynamics of demand-side participation have also been incorporated in our study, in which two utility-based indices are defined to quantify the effect of such interdependency. Using a probabilistic propagation technique, the different types of uncertainties involved can be normalized and systematically addressed under the same framework. Then, the relevant models can be applied to the CC evaluation procedures, wherein two dispatching schemes (i.e., reliability-driven and coordinated management) are considered to study the effect of DR operation on its CC. The proposed methodology is tested on a modified RTS system, and the obtained results confirm its effectiveness. [ABSTRACT FROM AUTHOR]

An accurate battery remaining useful life (RUL) estimation can facilitate the design of a reliable battery system as well as the safety and reliability of actual operation. A reasonable definition and an effective prediction algorithm are indispensable for the achievement of an accurate RUL estimation result. In this paper, the analysis of battery terminal voltage curves under different cycle numbers during charge process is utilized for RUL definition. Moreover, the relationship between RUL and charge curve is simulated by feed forward neural network (FFNN) for its simplicity and effectiveness. Considering the nonlinearity of lithium-ion charge curve, importance sampling (IS) is employed for FFNN input selection. Based on these results, an online approach using FFNN and IS is presented to estimate lithium-ion battery RUL in this paper. Experiments and numerical comparisons are conducted to validate the proposed method. The results show that the FFNN with IS is an accurate estimation method for actual operation. [ABSTRACT FROM AUTHOR]

The prospects of incorporating a consumer side load-scheduling algorithm that works in conjunction with the unit commitment problem, which in turn coordinates with real-time load balancer, are discussed in this paper. An integrated framework for an autonomous microgrid with objectives of increasing stability, reliability and economy is proposed. From the microgrid operators’ point of view, the load side scheduling helps reduce the stress on the system especially during peak hours thereby ensuring system stability and security. From the consumers’ point of view, the dynamic electricity prices within a day, which are a reflection of this time varying stress on the system, encourage them to endorse such a scheme and reduce their bills incurred. The unit commitment problem is run a day in advance to determine generator outputs for the following day. Owing to unpredictable weather conditions, running unit commitment problem in advance does not guarantee planned real-time generation in the microgrid scenario. Such variability in forecasted generation must be handled in any microgrid, while accounting for load demand uncertainties. To address this issue a load side energy management system and power balance scheme is proposed in this paper. The objective is to ascertain uninterrupted power to critical loads while managing other non-critical loads based on their priorities. [ABSTRACT FROM AUTHOR]

Energy storage devices, such as batteries and flywheels, are promising options for providing operating reserves due to their fast response and low emissions during operation. However, because of the complex nature of power systems, adding energy storage-based reserves to the power system may not necessarily benefit the environment. In this paper, we analyze these impacts in a test system and identify important drivers that affect the environmental outcomes. Dispatch results are obtained by solving an optimal power flow (OPF) problem and are used to determine emissions. We find that the impacts of adding energy storage are highly case-dependent. In systems with high renewable penetration levels and significant renewable curtailment, adding energy storage reduces emissions; in other systems, the impacts on emissions could be positive, neutral, or negative. The analyses presented in this paper show that policies to procure energy storage as a means to reduce emissions may actually lead to increased system-wide emissions if current dispatch algorithms are used. We also explore the impacts of modifying the dispatch algorithm to ensure system emissions with energy storage are no worse than system emissions without energy storage. [ABSTRACT FROM AUTHOR]

Recently, large-scale renewable energy resources have been widely integrated into power systems. To optimize large-scale integration of these resources and improve the operation performance of the distribution system, this paper proposes a novel dynamic energy management strategy with the cooperative interaction of an energy system: a multi-grid connected microgrids (MGs)-based active distribution system (ADS). A bi-level multi-objective optimization problem of the strategy is formulated with the active distribution network (ADN) in the upper level and MGs in the lower level. The interaction can be classified into two categories: the one between MGs and the ADN and the other one among MGs. The former is described by bi-level programming; the latter is innovatively explained by an interactive energy game matrix (IEGM) defined in this paper. The concept of the expanded energy storage system is defined and applied to the IEGM for the optimal operation of ADSs. The optimal operation includes improved technical performances in terms of power quality, energy utilization, adaptability and autonomy. The optimization problem is solved with a hybrid algorithm of Rough Set Theory–Hierarchical Genetic Algorithm–NSGA-II. Case studies of an ADS with different MGs and a real system would validate the efficiency of the proposed methodology. Results show that the proposed EM strategy can accurately quantify and guide the energy interaction among MGs and that between MGs and the ADN. Moreover, those technical performances of the ADS are improved. [ABSTRACT FROM AUTHOR]

This paper proposes the use of state space models to generate scenarios for the analysis of wind power plant (WPP) generation capabilities. The proposal is rooted on the advantages that state space models present for dealing with stochastic processes; mainly their structural definition and the use of Kalman filter to naturally tackle some involved operations. The specification proposed in this paper comprises a structured representation of individual Box–Jenkins models, with indications about further improvements that can be easily performed. These marginal models are combined to form a joint model in which the dependence structure is easily handled. Indications about the procedure to calibrate and check the model, as well as a validation of its statistical appropriateness, are provided. Application of the proposed state space models provides insight on the need to properly specify the structural dependence between wind speeds. In this paper the joint and marginal models are smoothly integrated into a backward–forward sweep algorithm to determine the performance indicators (voltages and powers) of a WPP through simulation. As a result, visibly heavy tails emerge in the generated power probability distribution through the use of the joint model—incorporating a detailed description of the dependence structure—in contrast with the normally distributed power yielded by the margin-based model. [ABSTRACT FROM AUTHOR]

This paper presents a review of the optimization techniques and strategies applied to wind turbine performance optimization. The topic is addressed by identifying the most significant objectives, targets and issues, as well as the optimization formulations, schemes and models available in the published literature. The current energy demand combined with depletion of fossil-fuel reserves and stricter environmental regulations have led to the development of alternative renewable energy solutions like wind energy. The current 2030 United States target is to have at least 20% of the US energy supply by onshore and offshore wind farms. To meet these demands, wind energy costs have to be able to compete with traditional fossil fuel sources. Hence, it is essential and vital that wind turbine designers and manufactures search the optimal solution that fits the objectives under a set of design constraints. Throughout the last 30 years, the objective function has evolved from the earlier maximized metric of the power coefficient to the maximization of the annual energy production. Common alternatives such as blade mass minimization and maximization of the rotor thrust and torque have been examined. However, the main objective has been focused on the minimization of the cost of energy in order for wind energy to become more competitive and economically attractive. The purpose of this paper is to review previous work that undertakes the performance optimization of horizontal wind turbines by highlighting the main aspects when tackling the wind turbine optimization problem such as: objective functions, design constraints, tools and models and optimization algorithms. In addition, in a conclusion of the review, a discussion and argument about the challenges, issues and future developments are identified. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • The real-time simulation framework for active distribution networks was built. • A multirate simulation method was proposed with high-accuracy and stability. • The detailed hardware design of the real-time simulator was presented. Abstract Real-time simulation of large-scale active distribution networks exhibiting a wide range of time-scales puts forward higher requirements for simulation accuracy and efficiency. This paper presents an extendable method and design for the real-time simulation of active distribution networks utilising high-performance hardware field programmable gate arrays. In the aspect of numerical algorithm, a high-accuracy and stable multirate simulation algorithm is proposed. The entire active distribution network is decoupled into different subsystems by their inherent time-scales and distinct time steps are used to solve the subsystems. Then root-matching method is adopted to form the exponential difference equations that represent the behaviours of the electric distribution network being modelled, which eliminates the truncation errors and thus provides a highly accurate time-domain solution. To handle the interface between the subsystems, a multirate interfacing method is proposed. The hardware design of the multirate interfaces is presented as well. With the multirate algorithm, a fully functional extendable real-time simulator based on field programmable gate arrays is designed and implemented. Two modified IEEE 33-node systems with photovoltaics and energy storage are simulated on the 4-field-programmable-gate-arrays-based real-time simulator. Simulation results are compared with the commercial simulation tool PSCAD/EMTDC to validate the correctness and effectiveness of the proposed method and design. [ABSTRACT FROM AUTHOR]

Highlights • An architecture of microgrid energy management system was established. • The architecture was designed for China in the context of Energy Internet. • Two-stage robust optimization model and algorithm were proposed. • Case study was conducted using a real case of Beijing Jinfeng Industrial Park. Abstract In order to address the practical challenges posed by the increasing penetration of distributed energy resources and electric vehicles, the evolution from traditional power systems to Energy Internet and the rapidly changing market and policy environments in China, this paper proposes an Energy-Internet-oriented architecture of microgrid energy management system for China, considering specific technical requirements, markets and policies in China. Following the proposed architecture, the robust operation of a multi-energy microgrid with renewable energy and load uncertainties is formulated as a two-stage robust optimization problem and the column and constraints generation algorithm is used to solve it. A real-life case study in China, Beijing Goldwind Industrial Park, is conducted to demonstrate the proposed architecture and method. Simulation results show that by exporting surplus generation to the external customers and bulk power grid, as an important feature of the market dimension of the proposed architecture, the revenue of the microgrid is improved significantly compared to the traditional business mode. Moreover, as important features of technology and regulation dimensions of the proposed architecture, it is shown that smart charging of electric vehicles is able to improve the revenue of the microgrid as well. Finally, the results also show that the proposed robust operation method achieves robustness at the cost of lower revenue in non-worst scenarios. [ABSTRACT FROM AUTHOR]

Highlights • We deal with a problem of representative measuring points selection for long-term wind power analysis. • We select the best set of N measuring points which produce the least wind power reconstruction error. • We apply a novel meta-heuristic for solving the problem. • The Analogue Method is used as wind power field reconstruction algorithm. • Results for Europe with ERA-Interim reanalysis data are reported. Abstract In this paper we deal with a problem of representative measuring points selection for long-term wind power analysis. It has direct applications such as wind farm prospective location or long-term power generation prediction in wind-based energy facilities. The problem’s objective is to select the best set of N measuring points (i.e. N representative points), in such a way that a wind power error reconstruction measure is minimized, considering a monthly average wind power field. In order to solve this problem, we use a novel meta-heuristic algorithm, the Coral Reefs Optimization with Substrate Layer, which is an evolutionary-type method able to combine different search procedures within a single population. The CRO-SL is hybridized with the Analogue Method as wind power reconstruction method, to identify the most representative points for the wind field. The proposed approach has been tested in the reconstruction of monthly average wind power fields in Europe, from reanalysis data (ERA-Interim reanalysis). The method exhibits strong performance as evidenced from the experiments carried out. The solutions obtained show that the more significant measuring points are mainly located over the Atlantic ocean, which is consistent with the wind speed climatology of the Northern hemisphere mid-latitudes. We have also analyzed the set of least representative points to reconstruct the wind power field (less informative points for whole reconstruction of the field), obtaining points mainly located at the North of Scandinavia (which may be associated with the circumpolar circulation), and some points in the Eastern Mediterranean, which seem to be related to the Etesian winds. Reconstructions at seasonal scales show similar results, which provides confidence on the robustness of the proposed method. The proposed methodology can be further applied to alternative energy-related problems, such as the selection of critical energy infra-structures or the selection of critical points for climate change studies, among others. [ABSTRACT FROM AUTHOR]

Highlights • An adaptive Fuzzy Logic PID approach is proposed to control steam turbines. • A CSPP has been modeled focusing on power loop with variable steam conditions. • Design of FPID controller through the knowledge acquired during simulation phase. • FL allows to control steam turbines in off-design points with optimal performance. • Control algorithm with reduced design and implementation time on PLC platforms. Abstract In Concentrated Solar Power Plants, steam turbines controlled with standard Proportional Integrative Derivative (PID) methods may suffer from performance downgrading in power generation when the steam conditions deviate from nominal ones. An enhancement of standard steam turbine controller can be the key to achieve optimal performance also in non-nominal steam conditions. This paper presents the improvement of the PID control concept by exploiting Fuzzy Logic, an artificial intelligence technique that allows taking into account the human experience and knowledge on the system behavior. A real Concentrated Solar Power Plant has been modeled focusing on generated power control loop, its stability and performance analysis, knowledge useful to design a Fuzzy Inference System. A fuzzy logic controller is proposed to continuously adapt the PID parameters, to improve the steam turbine governor action. Its performance is compared to the classical PID tuned according to three different approaches. The fuzzy logic PID controller extends the simplicity of PID and adapts the control action to actual operating condition by providing the system with a sort of “decision-making skill”. The possibility to design implementable algorithms on a Programmable Logic Controller, which have stringent computational speed and memory requirements, has been explicitly taken into account in the developed work, through the minimization of the controller complexity with a reduced number of fuzzy sets and fuzzy rules within the fuzzy inference system. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • An improved Li-ion battery model is proposed. • A support vector regression method is employed to estimate the state-of-health. • The particle swarm optimization is used to optimize the global parameters. • Comparison with other methods shows the advantage of the proposed method. Abstract Lithium-ion batteries have been widely used in many fields. The state-of-health is necessary and important for battery performance evaluation and lifetime prediction. A reliable state-of-health estimation is essential to help batteries work in a safe and suitable condition. In this paper, a novel state-of-health estimation approach is proposed for lithium-ion batteries based on statistical knowledge. An improved battery model, which combines the open-circuit-voltage modeling and the Thevenin equivalent circuit model, is proposed to improve the model accuracy and study the relation between internal parameters and states of the battery. The joint extended Kalman filter-recursive-least squares algorithm is employed to estimate battery state-of-charge and identify the model parameters and open-circuit-voltage simultaneously. Then a particle swarm optimization-least square support vector regression approach is employed to give a reliable state-of-health estimation result with high accuracy and good generalization ability, where the particle swarm optimization algorithm is used to improve the algorithm ability of global optimization. In order to verify the accuracy of the proposed method, static and dynamic current profile tests are carried out on lithium iron phosphate batteries in different aging levels. The experimental results indicate that the proposed method can present suitability for state-of-health estimation with high accuracy. [ABSTRACT FROM AUTHOR]

Highlights • A model based on the minimization of Gibbs free energy has been developed. • The algorithm implemented in a free open source platform is presented. • The model allows the evaluation of pollutants besides energy related species. Abstract This paper presents a non-stoichiometric equilibrium model for the simulation of the biomass gasification process in downdraft gasifiers to be used within simulation models of complex energy systems. The chemical equilibrium is determined by minimizing the Gibbs free energy. The feedstock is composed of five elements, while fifteen chemical species are considered for the syngas. The model calculates the relative quantities of gasification products and the lower heating value of the syngas. An advantage of the non-stoichiometric approach is that it can easily calculate not only the concentrations of the main gasification products, but also the concentrations of minor products, especially the pollutant chemical species containing nitrogen and sulfur. To analyse the behaviour of the model, a sensitivity analysis of its process parameters is presented. The model is then validated by comparing its results with the results of the simulation carried out with equilibrium models and with experimental data found in literature. Finally, the model is applied to the study of the gasification of forest waste. [ABSTRACT FROM AUTHOR]

In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning models for predicting electricity prices and we show how they lead to improvements in predictive accuracy. In addition, we also consider that, despite the large number of proposed methods for predicting electricity prices, an extensive benchmark is still missing. To tackle that, we compare and analyze the accuracy of 27 common approaches for electricity price forecasting. Based on the benchmark results, we show how the proposed deep learning models outperform the state-of-the-art methods and obtain results that are statistically significant. Finally, using the same results, we also show that: (i) machine learning methods yield, in general, a better accuracy than statistical models; (ii) moving average terms do not improve the predictive accuracy; (iii) hybrid models do not outperform their simpler counterparts. [ABSTRACT FROM AUTHOR]

Motivated by the high electric vehicle (EV) penetration percentages foreseen for the near future, this paper studies the participation of large fleets of EVs in electricity day-ahead markets. Specifically, we consider a scenario where a number of independent and self-interested EV aggregators participate in the day-ahead market to purchase energy to satisfy their clients’ driving needs. In this scenario, independent bidding can drive prices up unnecessarily, resulting in increased electricity costs for all participants. Inter-aggregator cooperation can mitigate this by producing coordinated bids. However, this is challenging due to the self-interested nature of the aggregators, who may try to manipulate the system in order to obtain personal benefit. In order to overcome this issue, we employ techniques from mechanism design to develop a coordination mechanism which incentivises self-interested EV aggregators to report their energy requirements truthfully to a third-party coordinator. This coordinator is then able to employ a day-ahead bidding algorithm to optimise the global bids on their behalf, extending the benefits of smart bidding to groups of competing EV aggregators. Importantly, the proposed coordination mechanism is straightforward to implement and does not require any additional infrastructure. To ensure scalability and computational tractability, a novel price-maker day-ahead bidding algorithm is proposed, which is formulated in terms of simple energy requirement constraints. The coordination mechanism substantially reduces bidding costs, as shown in a case study which uses real market and driver data from the Iberian Peninsula. [ABSTRACT FROM AUTHOR]

As the increasing penetration of renewable energy sources in distribution network, the tie-line power fluctuation caused by intermittent renewable energy produces a detrimental impact on security and reliability of the main grid. Therefore, maximization of renewable energy utilization as well as stabilization of tie-line power fluctuation are both required in the distribution network, to ease the power regulation burden of main grid. Remarkably, the development of integrated energy systems makes it possible to transfer the fluctuation in power network to other larger inertial systems. This paper proposes a coordinated operation strategy for the gas-electricity integrated distribution system, considering AC power flow in the power network and the gas hydraulic calculation in gas network. In addition, based on the nonlinear multi-energy coupling external characteristics modeling of CCHP system, the power fluctuation of renewable energy sources is transferred to gas distribution network and cooling or heating system by coordinated operation of Multi-CCHPs. Moreover, a two-stage optimization algorithm is proposed to solve the corresponding mixed integer nonlinear programming (MINLP) problem. The simulations conducted on 33-node power distribution network and 24-node gas network show that the proposed method can effectively smooth the tie-line power fluctuation in variety case. [ABSTRACT FROM AUTHOR]

Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive accuracy in a local market. To measure the importance of these features, we propose a novel feature selection algorithm that, by using Bayesian optimization and functional analysis of variance, evaluates the effect of the features on the algorithm performance. In addition, using market integration, we propose a second model that, by simultaneously predicting prices from two markets, improves the forecasting accuracy even further. As a case study, we consider the electricity market in Belgium and the improvements in forecasting accuracy when using various French electricity features. We show that the two proposed models lead to improvements that are statistically significant. Particularly, due to market integration, the predictive accuracy is improved from 15.7% to 12.5% sMAPE (symmetric mean absolute percentage error). In addition, we show that the proposed feature selection algorithm is able to perform a correct assessment, i.e. to discard the irrelevant features. [ABSTRACT FROM AUTHOR]

In the competitive power industry environment, electricity price forecasting is a fundamental task when market participants decide upon bidding strategies. This has led researchers in the last years to intensely search for accurate forecasting methods, contributing to better risk assessment, with significant financial repercussions. This paper presents a hybrid method that combines similar and recent day-based selection, correlation and wavelet analysis in a pre-processing stage. Afterwards a feedforward neural network is used alongside Bat and Scaled Conjugate Gradient Algorithms to improve the traditional neural network learning capability. Another feature is the method’s capacity to fine-tune neural network architecture and wavelet decomposition, for which there is no optimal paradigm. Numerical testing was applied in a day-ahead framework to historical data pertaining to Spanish and Pennsylvania-New Jersey-Maryland (PJM) electricity markets, revealing positive forecasting results in comparison with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Heat loss from rotary kilns accounts for certain amounts of the total energy consumption in chemical and metallurgical industries. To reduce the heat loss, a parallel and a series-parallel heat recovery systems with nine heat recovery exchangers are proposed to preheat the cold water in this paper. Experimental measurements are carried out to determine the heat transfer coefficient equations of each heat recovery exchanger. Then, the heat recovery systems are analyzed to deduce the mathematic relation between the design parameters and the system requirements, i.e. the temperatures and heat transfer rates of the nine heat recovery exchangers. The total heat transfer area, the total power consumption and the entropy generation due to heat transfer and fluid flow are set as the objective functions in four multi-objective optimization (MOO) cases. With the aid of the genetic algorithm in the Matlab 2015, the optimized operational and structural parameters are obtained. Finally, the MOO results are compared with that of the single objective optimization (SOO) method and the original values. The optimization results show that the MOO method are more suitable for the operational parameters design of the heat recovery systems compared with the SOO method. The required total heat transfer area and the total power consumption are decreased by at least 12.1% and 13.7%, respectively. Besides, as the entropy generation due to heat transfer and fluid flow decrease in the MOO cases, the corresponding heat transfer area and power consumption of the heat recovery system decrease, respectively. [ABSTRACT FROM AUTHOR]

As an integral feature of a future smart grid, demand response (DR) provides utility companies an emerging alternative to boost the reliability performance (e.g., supply adequacy) of power systems. Unlike for physical devices, the availability of DR is not only dependent on the operations of electric appliances on the demand side but can be affected by customers’ behaviors. Thus, how or to what extent DR actions can affect power system reliability becomes an important issue for utilities. In this paper, a new approach for assessing the contribution of incentive-based DR to the supply adequacy of smart distribution systems (SDS) is presented. Compared with existing methods, our method explicitly captures the varying availability of customer DR capabilities. We introduce a behavior-reinforcement procedure to model the correlation of users’ participation willingness with their historical DR profitability. Then, a strategic DR dispatch strategy can be developed to optimize users’ DR availability bids in the market. In addition, the interdependency between a communication system and DR operation has also been considered. A hybrid algorithm based on sequential Monte-Carlo simulation and an optimal load dispatch method is employed to evaluate the system reliability in this context. The proposed approach is illustrated using both a small-scale test case and a real regional distribution grid in China. The results demonstrate the effectiveness of the presented method. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for the optimization of the layout of offshore wind farms. Almost all previous work on optimal micro-siting for large offshore wind farms have been based on irregular arrangements of wind turbines. However, most offshore wind farms already built are configured in symmetrical/regular layouts. From a mathematical point of view, the geometrical relationships of such symmetrical layouts enable the problem to be defined by just a few variables. This presents a considerable advantage compared with irregular arrangements where the number of variables is directly linked to both the number of wind turbines and the number of cells in which the computational domain is discretized. In contrast, symmetrical layouts are more demanding with regard to the optimization process, since the problem constraints, such as the shape of the available exploration area to deploy the project, the maximum surface allowed, and the maximum number of wind turbines, drastically increase the non-linearity of the objective function, which affects the ability of the optimization algorithm to achieve the optimal solution. This work compares the behaviour of two meta-heuristic optimization algorithms (the Genetic Algorithm and Particle Swarm Optimization) in solving the addressed problem and, more importantly, it introduces a series of improvements on the objective function, which enhance the behaviour of the optimization algorithms when dealing with realistic constraints, such as the shape of the concession zone and maximum deployable area. Finally, the performance of the proposed methodologies has been tested under two situations. The first scenario is a small-sized hypothetical offshore wind farm. In the second scenario, the layout of a real project (Horns Rev 3 offshore wind farm) has been optimized and compared with the solutions proposed by the Danish transmission system operator. The results obtained show the ability of the proposed tools to successfully show the ability of the proposed tools to optimize offshore wind farms under realistic considerations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel game model based on the hierarchical Stackelberg game for analyzing the multiple energies trading (MET) problem in integrated energy systems (IESs). In the proposed game model, a number of distributed energy stations (DESs) lead the game deciding the unit prices of electricity and cooling energies they generated, while multiple energy users (EUs) perform as followers determining the amounts of energies to consume. In addition to maximizing the profit of each DES, the proposed hierarchical game model also considers the benefit of each EU, who actively participates in the MET. We prove that, for the first time, there exists a unique Stackelberg Equilibrium (SE) in the MET, so that the existence of an equilibrium strategy optimizing the objectives of all participants can be guaranteed. Moreover, we found that the price setting game played by DESs is a submodular game when energy dispatching is performed during their generation process, which indicates that the behaviors of DESs are strategic substitutes. Furthermore, the SE is obtained in a closed-form, by which the effects of coupling generation of multiple energies are analyzed. Finally, a best response algorithm is provided to obtain the SE in an iterative way. Numerical studies demonstrate the convergency of the proposed best response algorithm, corroborate the jointly effects of market scale and exogenous parameter on the SE and verify the practicability of the proposed game-theoretic approach. [ABSTRACT FROM AUTHOR]

The using of solar energy has been increased since it is a clean source of energy. In this way, the design of photovoltaic cells has attracted the attention of researchers over the world. There are two main problems in this field: having a useful model to characterize the solar cells and the absence of data about photovoltaic cells. This situation even affects the performance of the photovoltaic modules (panels). The characteristics of the current vs. voltage are used to describe the behavior of solar cells. Considering such values, the design problem involves the solution of the complex non-linear and multi-modal objective functions. Different algorithms have been proposed to identify the parameters of the photovoltaic cells and panels. Most of them commonly fail in finding the optimal solutions. This paper proposes the Chaotic Whale Optimization Algorithm (CWOA) for the parameters estimation of solar cells. The main advantage of the proposed approach is using the chaotic maps to compute and automatically adapt the internal parameters of the optimization algorithm. This situation is beneficial in complex problems, because along the iterative process, the proposed algorithm improves their capabilities to search for the best solution. The modified method is able to optimize complex and multimodal objective functions. For example, the function for the estimation of parameters of solar cells. To illustrate the capabilities of the proposed algorithm in the solar cell design, it is compared with other optimization methods over different datasets. Moreover, the experimental results support the improved performance of the proposed approach regarding accuracy and robustness. [ABSTRACT FROM AUTHOR]

Abstract: With the availability of powerful and low cost computing power, maximum power point tracking (MPPT) that utilizes soft computing (SC) techniques are attracting substantial interests from the PV communities. Due to their flexibility and ability to handle non-linear problems, robust SC-based MPPT schemes can be developed. Furthermore, the adaptive in nature SC algorithms is suitable in handling adverse environmental conditions such as partial shading and rapid changes in irradiance. To date, there are several works on MPPT using SC from which we select approximately 45 published works that are directly related to MPPT. However, information on these methods are scattered and there appears to be an absence for a comprehensive review paper on this topic. This work summarizes the current technology and status of SC MPPT as reported in various literature. It also provides an evaluation on the performance of various SC methods based on several criteria, namely PV array dependency, convergence time, ability to handle partial shading conditions, algorithm complexity and hardware/practical implementation. It is envisaged that the information gathered in this paper will be a valuable one-stop source of information for researchers, as well as providing a direction for future research in this area. [Copyright &y& Elsevier]

Short-term building cooling load prediction is the essential foundation for many building energy management tasks, such as fault detection and diagnosis, demand-side management and control optimization. Conventional methods, which heavily rely on physical principles, have limited power in practice as their performance is subject to many physical assumptions. By contrast, data-driven methods have gained huge interests due to their flexibility in model development and the rich data available in modern buildings. The rapid development in data science has provided advanced data analytics to tackle prediction problems in a more convenient, efficient and effective way. This paper investigates the potential of one of the most promising techniques in advanced data analytics, i.e., deep learning, in predicting 24-h ahead building cooling load profiles. Deep learning refers to a collection of machine learning algorithms which are powerful in revealing nonlinear and complex patterns in big data. Deep learning can be used either in a supervised manner to develop prediction models with given inputs and output (i.e., cooling load), or in an unsupervised manner to extract meaningful features from raw data as model inputs. This study exploits the potential of deep learning in both manners, and compares its performance in cooling load prediction with typical feature extraction methods and popular prediction techniques in the building field. The results show that deep learning can enhance the performance of building cooling load prediction, especially when used in an unsupervised manner for constructing high-level features as model inputs. Using the features extracted by unsupervised deep learning as inputs for cooling load prediction can evidently enhance the prediction performance. The findings are enlightening and could bring more flexible and effective solutions for building energy predictions. [ABSTRACT FROM AUTHOR]

Non-intrusive load monitoring (NILM) is a novel and cost-effective technology for monitoring load electricity energy consumption details. In the event-based NILM, transient power waveform (TPW) time-series can be used as signatures to identify the transients of the electrical appliances in the aggregated load, and then to determine their operating states, estimate their power demand and cumulative energy consumption. In this paper, for load transient identification, the dynamic time warping (DTW) algorithm is adopted for the first time to measure the similarity between the variable-length raw TPW sample and template time-series. Accordingly, a nearest neighbor transient identification method is proposed to identify the appliance creating the TPW sample time-series, in which the DTW-based integrated distance is used to measure the similarity of TPW signatures. Three schemes to calculate the integrated distance are designed, combining multiple types of TPW signatures. Comparison tests with existing methods are conducted using public datasets. The comparison test results indicate that the proposed load transient identification method cannot only improve the accuracy of load transient identification, but also is easy to implement at a reasonable cost. Ultimately, the proposed method is implemented in an embedded system. The field test results show that it can identify the operating states of electrical appliances with high accuracy. [ABSTRACT FROM AUTHOR]

The objective of this paper is to propose a novel methodology to compute the benefit obtained by the individual transmission network users from each of the transmission expansion projects within an expansion plan. The benefits computed should be coherent with the technical and economic principles that underlie the development of the expansion plan. Thus, this methodology is based on the idea that the benefits produced by each project of a plan should be determined considering all projects jointly, instead of individually. Some benefits obtained by users from projects evolve continuously with the deployment of the expansion plan, while others are discrete, since they occur at certain points of the deployment of this plan. A separate Aumann-Shapley game is solved to allocate continuous benefits, and each discrete one. In the second case, the standard Aumann-Shapley algorithm for the allocation of benefits is modified to cope with the fact that the function of each user’s benefits is not continuous with the size of projects deployed. Two case studies are used to compare the methodology proposed with existing ones and demonstrate its applicability to real-life decision making processes. The results show that the methodology proposed is able to overcome problems detected in other methodologies, providing more accurate and sound results. The good properties of the methodology proposed make it applicable to problems related to network expansion regulation, such as the cost allocation of new investments. Although the methodology proposed is particularized to electric power systems, its concept and fundamentals can also be applied in other energy sectors, such as gas. [ABSTRACT FROM AUTHOR]