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The effective load prediction capability of Energy Aggregation Service Providers is fundamental to their participation in the electricity market and a crucial factor in maximizing their market profits. However, a major challenge we face is that the data required for prediction is typically scattered across individual energy entities, and due to concerns regarding data security and privacy, these entities are often unwilling to share their data. This issue hampers the operational efficiency of Energy Aggregation Service Providers and also impedes the learning and analysis of relevant energy supplier operational patterns. To address this problem, this paper proposes a Federated Learning-based load prediction method for Energy Aggregation Service Providers, allowing them to share information without accessing the actual datasets, thereby obtaining accurate and effective load prediction capabilities. Firstly, an artificial neural network with multidimensional environmental feature selection is established based on task requirements. Then, a weighted average strategy is employed to jointly train the artificial neural network, bridging the gap between individual energy entities and environmental features. Finally, the trained model features are sent to the server of the energy aggregation service provider for fitting. Through a case study using partial data from a large-scale energy aggregation service provider in a southern city of China, the results demonstrate the effectiveness of the proposed method in improving overall operational efficiency and average prediction accuracy.

In a power system, when extreme events occur, such as ice storm, large scale blackouts may be unavoidable. Such small probability but high risk events have huge impact on power systems. Most resilience research in power systems only considers faults on the physical side, which would lead to overly idealistic results. This paper proposes a two-stage cyber-physical resilience enhancement method considering energy storage (ES) systems. The first stage calculates optimal planning of ES systems, and the second stage assesses resilience and enhancement of ES systems during the disaster. In the proposed model, cyber faults indirectly damage the system by disabling monitoring and control function of control center. As a result, when detection and response process of physical faults are blocked by cyber failures, serious load shedding occurs. Such a cyber-physical coupling mechanism of fault, response, restoration process is demonstrated in the modified IEEE Reliable Test System-79 (RTS-79). Simulation results show compared with the physical-only system, the cyber-physical system has a more accurate but degraded resilient performance. Besides, ES systems setting at proper place effectively enhance resilience of the cyber-physical transmission system with less load Shedding.

High-cost generator units are at a cost disadvantage in electricity spot markets. This study focuses on revenue mechanisms of gas-fired units affected by power market reform in Guangdong province, China. For the first time, we compare impacts on market indicators of five settlement mechanisms: feed-in tariff (FiT), location marginal price (LMP), contract for difference (CFD), direct subsidy (DS), and estimated revenue method (ERM). In particular, we design key parameters, including authorized CFD and ERM of two kinds of government authorized contracts based on traditional dispatching patterns to avoid significant profit fluctuations brought by market reforms. We also analyze impact of factors such as climbing performance, seasonal load, and subsidy amount on the overall market and its players. Results of a case study show to directly subsidize gasfired units will lead higher-cost units to generate more electricity, with a resulting loss of social welfare. Disruption of market prices and provision of unreasonable incentives are fatal disadvantages of this subsidy method. The government and policymakers should consider financial means to adjust benefits to reduce production costs and increase social welfare. Also, by case analysis, the ERM shows its stable performance in revenue of high-cost units, while we find that authorized CFD is not applicable for gas-fired units whose output is unstable as a marginal unit frequently. Therefore, we suggest government agencies adopt ERM to sign contracts with gas-fired units, to attain a balance between fairness and efficiency.

Abstract This paper concentrates on the optimal bidding strategy of a plug‐in electric vehicle aggregator (PEVA) using indirect load control in the day‐ahead energy market, which is generally formulated as bilevel programming. However, this bilevel model is nonconvex and intractable due to the complementary constraints imposed to preclude plug‐in electric vehicles (PEVs) from simultaneously charging and discharging. To handle this problem, an exact relaxation method is first proposed to remove the complementary constraints of the PEVs' charging and discharging behaviours. To further reduce the complexity of the model, a method is proposed for the exact relaxation of the complementary slackness of Karush‐Kuhn‐Tucker (KKT) conditions in the reformulated mathematical program with equilibrium constraints. By removing the complementary constraints, the relaxation methods can effectively reduce the complexity of the model and improve the computational performance. The results of the case study demonstrate the exactness and efficiency of the proposed relaxation methods. Specifically, in the test case with 1000 PEVs and 20 scenarios, 220,000 constraints are removed by the two exact relaxation methods. Moreover, using the second exact relaxation method, in the test case with 400 PEVs and 60 scenarios and the case with 1000 PEVs and 60 scenarios, the computational time drops by 30.3% and 23.3%, respectively.

The propagation of meteorological droughts to soil droughts poses a substantial threat to water resources, agricultural production, and social systems. Understanding drought propagation process is crucial for early warning and mitigation, but mechanisms of the propagation from meteorological drought to soil drought, particularly at varying soil depths, remain insufficiently understood. Here, we employ the maximum correlation coefficient method and the random forest (RF) model to investigate the spatiotemporal patterns and drivers of propagation time (PT) from meteorological drought to soil drought at four different depths across China from 1980 to 2018. Our findings reveal consistently higher PT in northern China and lower PT in southern China across varying soil depths, with more pronounced spatial heterogeneity with increasing soil depth. Furthermore, we identify temperature and precipitation as determinants of spatial patterns of PT in surface and deeper soil layers, respectively. Additionally, precipitation emerges as the dominant factor influencing changes in PT between different soil layers. Our study highlights a discernible shift in PT drivers from temperature to precipitation as soil depth increases and the significant impact of precipitation on exacerbating spatial heterogeneity in PT. This study contributes to an enhanced comprehension of the propagation process from meteorological drought to soil drought at different depths, which can aid in establishing practical drought mitigation measures and early warning systems.

In order to deal with the volatility caused by the high proportion of renewable energy, Europe has established a cross-regional balancing market. The European experience and practices in the cross-regional balancing market can bring reference for the construction of China’s power market. To this end, this article conducts an in-depth study on the operation mode and settlement of the unified European balance market, and reviews the key issues and the corresponding solutions faced by the settlement level in the European integration of the balancing markets of various countries. Finally, founded on the actual situation of China’s inter-regional power market, the European balancing market is proposed for the construction of China’s inter-regional market.

Nowadays, the transaction scale of the cross-provincial electricity market in China has increased, and the problem of transmission capacity allocation attracts the government’s attention. During the development of the European unified electricity market, capacity allocation methods based on bidding zones such as Available Transfer Capacity (ATC) and Flow-Based Market Coupling (FBMC) have realized regional market coupling and improved grid utilization. This article intends to analyze the allocation methods of transmission capacity in the European electricity market, focusing on the comparison between the ATC and the FMBC, considering the actual background of the Chinese electricity market, especially the differences between the Chinese market and the European market in the grid structure and marketization process, it is more suitable to adopt ATC in China and it makes the suggestion for the application of the capacity allocation method. Besides, the idea of bidding zone simplification is also worth learning. It is also recommended that provincial institutions exchange data related to capacity allocation to coordinate the calculation of trading domains.

The design of the power generation capacity adequacy mechanism requires comprehensive consideration of political goals, power systems, power markets and other factors. Although various adequacy mechanisms have been developed and practiced, they all suffer from certain problems. After a comprehensive investigation of the mechanisms of scarcity pricing and capacity market, this paper proposes a novel power generation capacity adequacy mechanism based on revenue call options, which is divided into two processes: forward contract auction and short-term market settlement. The forward contract auction can guarantee participants a steady revenue, and the short-term market settlement process is combined with scarcity pricing to provide accurate price signals. Then, the effectiveness and feasibility of this mechanism are verified by simulation analysis using data collected from Jiangsu Province. The results show that this mechanism can effectively restore the price signal, fix the economic risk, and ensure the unit to recover all the capacity cost and energy cost.

With the advancement of electricity market reform, eight pilot spot provinces in China have started electricity spot market trading. In the settlement rules of spot markets in each region, some areas apply nodal weighted average price to the settlement’s customer and supply sides. The economic incentive effect of the spot market pricing mechanism on market participants affects their bidding strategies. Therefore, based on the current research on electric spot market pricing mechanisms, this paper investigates the effects of bidding procedures and fairness of generator and consumption sides under three settlement mechanisms: locational marginal pricing, zonal pricing, and average system pricing. Through multi-agent-based electricity market simulation, the paper establishes a spot market clearing and settlement model to quantitatively analyze the impact of zonal settlement mechanisms on the interests of each market player. By validating analysis results, the paper makes suggestions for selecting electricity price mechanisms in China’s electric spot market environment.

Under the electricity market, generation companies (GenCos) maximize their own revenue through strategic bidding, and the Nash equilibrium (NE) formed by the game between GenCos is an effective tool to assist in the design and regulation of the electricity market. However, the power market bidding game is usually modeled as a nonlinear nonconvex complex model, and it is difficult for researchers to intuitively analyze the game process and discover the laws. In order to solve the above problems, this paper proposes a visualization method for the GenCos optimal offer model, based on which a visualization method for the GenCos bidding game process is proposed. The case study shows that the visualization-based analysis method can help researchers to visually infer the game evolution process, analyze the pattern of NE and evaluate the game results, and ultimately provide a reference for further market analysis.

From February 15 to 19, 2021, a rotating outage due to load-shed occurred in the power grid of Texas in the United States, causing widespread global concern. In this paper, an overview of the power grid and electricity market in Texas was introduced firstly. Then, this paper sorted out the occurrence and development process of the accident with time as the main line, and paid attention to the operation status of the electricity market during the accident period. Moreover, the causes of the three major problems such as imbalance between supply and demand, high electricity price, and financial crisis in the accident were analyzed. Finally, this paper summarized the experience and lessons of power outages, and put forward relevant enlightenments to the construction of China's electricity market.

This paper studies the optimal decision-making problem for a plug-in electric taxi (PET) in a time-varying complex environment, i.e., a passenger environment, charging station environment, traffic environment, and taxi company management system, in order to maximize PET profit in a short-term operating cycle. First, this problem is formulated as a sequential decision-making problem composed of multiple decision slots. Then, to make the model more practical, the model is divided into two parts: an external environment and an electric taxi model for refinement. The uncertainty and time-varying characteristics of four environmental aspects, including passengers, charging stations, traffic, and taxi company management systems, are analysed and modelled. The transitions between adjacent processes and the environmental feedback of each process are modelled by further subdividing both the serving process and the charging process of the PET into multiple subprocesses, including cruising, carrying passengers, driving to the charging station, queueing before charging, and connecting to the power grid for charging. There are several uncertain factors in the sequential decision-making process for the PET, which leads to difficulty in solving the problem. To address this difficulty, the model-free algorithm SARSA is chosen. Finally, the effectiveness of the proposed method is verified by simulation results.

An industrial park is one of the typical energy consumption schemes in power systems owing to the heavy industrial loads and their abilities to respond to electricity price changes. Therefore, energy integration in the industrial sector is significant. Accordingly, the concept of industrial virtual power plant (IVPP) has been proposed to deal with such problems. This study demonstrates an IVPP model to manage resources in an eco-industrial park, including energy storage systems, demand response (DR) resources, and distributed energies. In addition, fuzzy theory is used to change the deterministic system constraints to fuzzy parameters, considering the uncertainty of renewable energy, and fuzzy chance constraints are then set based on the credibility theory. By maximizing the daily benefits of the IVPP owners in day-ahead markets, DR and energy storage systems can be scheduled economically. Therefore, the energy between the grid and IVPP can flow in both directions: the surplus renewable electricity of IVPP can be sold in the market; when the electricity generated inside IVPP is not enough for its use, IVPP can also purchase power through the market. Case studies based on three wind-level scenarios demonstrate the efficient synergies between IVPP resources. The validation results indicate that IVPP can optimize the supply and demand resources in industrial parks, thereby decarbonizing the power systems.

The accuracy of the simulation model has a profound impact on the optimal operation of the energy hubs (EHs). However, in many articles, the constant model of the efficiency of equipment is adopted to formulate the operation system, which would probably lead to a simplification of the simulation models. But, EHs are typically operated under off-design condition due to the fluctuations in cooling, heating, electricity requirement. Moreover, even though the off-design characteristics are considered, few studies have suggested comparing the differences between those two models by considering the operation cost. In order to assess the effect of the off-design characteristics of EH on the optimal operation accuracy in this paper, two test cases are performed on the fixed and variable load conditions, respectively. In addition, the individual effect of off-design characteristics of each equipment on the optimal operation cost of the EH is also investigated through four optimization runs. It is worth mentioning that the optimal operation problem of the EH considering the off-design characteristics and on-off status of the equipment is a mixed integer non-linear programming problem (MINLP). By testing the design and off-design models on the two cases, the results of simulation demonstrate that the optimal operation cost for the off-design model is larger than that for the design model. Nonetheless, in the aspect of the authenticity of the system operation strategy, the off-design model performs better than the design model. Furthermore, a larger relative error of the system operation cost between the two models can be observed when the EH is operated under a relatively lower load condition, revealing that the influence of off-design characteristic on the optimal operation of EHs is too significant to be neglected.

In this paper, the financial sense of used batteries providing energy storage (ES) for grid applications is investigated. An investment strategy to determine the optimal site and size of used battery ES is proposed for profit-oriented merchant entities and is formulated as a bilevel model. On the upper level, the decisions on ES investment and bidding and offering in the market are optimized, subject to maximizing the social welfare, which is achieved in the market clearing process on the lower level. Computational tractability is achieved by implementing a solution approach based on Benders decomposition. To speed up the convergence of this algorithm, we propose two acceleration techniques, namely, the valid inequalities and a multi-cut framework. We test the proposed algorithm on the IEEE Reliability Test System. The optimal siting and sizing decisions of the used battery ES are analyzed, followed by the analyses of the impacts of the lifespan, unit cost, and capacity loss of the used batteries on the investment decisions. Finally, we demonstrate that the proposed accelerating Benders decomposition outperforms the classical decomposition methods on computational performance.

Abstract Currently, small islands are facing an energy supply shortage, which has led to considerable concern. Establishing an island microgrid is a relatively good solution to the problem. However, high investment costs restrict its application. In this paper, micro pumped storage (MPS) is used as an energy storage system (ESS) for islands with good geographical conditions, and deferrable appliance is treated as the virtual power source which can be used in the planning and operational processes. Household acceptance of demand response (DR) is indicated by the demand response participation degree (DRPD), and a sizing optimization model for considering the demand response of household appliances in an island microgrid is proposed. The particle swarm optimization (PSO) is used to obtain the optimal sizing of all major devices. In addition, the battery storage (BS) scheme is used as the control group. The results of case studies demonstrate that the proposed method is effective, and the DR of deferrable appliances and the application of MPS can significantly reduce island microgrid investment. Sensitivity analysis on the total load of the island and the water head of the MPS are conducted.

There is a hydraulic coupling relationship between the upstream and downstream of cascade hydropower stations, and they usually belong to different stakeholders. Traditional clearing mechanism may lead to the mismatch between the bid-winning power and the actual power generated by the downstream power stations, and the low-cost downstream power stations may lose some chance to generate, resulting in damage to social welfare. This paper analyzes the problems faced by multi-operator cascade hydropower stations participating in the day-ahead market. By deducing the generation coupling relationship between upstream and downstream cascade hydropower stations, it is found that the output of downstream hydropower stations can be divided into four parts: fixed part, adjustable part, the part coupled with the output of the upstream power station, the part coupled with the output of the upstream power station and the adjustable output. At last, day-ahead market clearing mechanism and settlement mechanism for downstream power stations to participate in bidding are proposed.

An integrated energy system, referred to specifically as a heterogeneous energy system that combines cooling, heating, power, etc., is a dynamic system containing continuous as well as discrete behaviors on both technical and economic levels. Currently, the comprehensive utilization of multiple forms of energy and the implementation of the energy market have made the simulation of such a system very complicated, which is reflected in two aspects. First, the simulation model becomes complex and varied. Second, the time-varying characteristics of the models are quite diverse. Therefore, a standard and normative modeling and simulation method is urgently needed. This work aims to obtain a compatible modeling and simulation method for the energy economy coupling system. The individual-based model is widely used to describe organisms in an ecology system that are similar to the energy–economy coupled system. Inspired by this, a general simulation approach based on the individual-based model is proposed in this paper to overcome these existing problems. The standard formal expression model is built, then its structure and elements explained in detail, and multi-scale time simulation supported to model and simulate an integrated energy system that is coupled with markets. In addition, a simulator is designed and implemented based on multi-agent framework and model-view-controller architecture. Finally, a simulation case of a conceived scenario was designed and executed, and the results analysis proved the validity and versatility of the proposed approach. The proposed method has the advantages of model standardization, multi-scale time compatibility, distributed simulation capability, and privacy protection. These advantages support and strengthen each other. Through these studies, a systematic approach was formed that could improve the standardization of modeling and simulation in the energy–economy research area.

Realizing the full potential of plug-in electric vehicle (PEVs) in power systems requires the development of business models for PEV owners and electric vehicle aggregators (EVAs). Most business models neglect the significant economic potential of PEV demand response. This paper addresses this challenge by proposing a novel business model to optimize the charging energy of PEVs for maximizing the owners’ profits. The proposed business model aims to overcome the opportunity cost neglect for PEV owners, whose charging energy and charging profiles are optimized with full consideration of the demand curves and market conditions. Lagrangian relaxation technology is used for the relaxation of the constraint of satisfying the charging demand, and as a result, the optimization potential becomes greater. The bidding/offering strategy is formulated as a two-stage stochastic optimization problem, considering the different market prices and initial and target state of energy (SOE) of the PEVs. By case studies and analyses, we demonstrate that the proposed business model can effectively overcome the opportunity cost neglect and increase the PEV owners’ profits. Furthermore, we demonstrate that the proposed business model is incentive-compatible. The PEV owners will be attracted by the proposed business model.

The simulation model is one of the key points affecting the optimal planning and operation of energy hubs (EHs). Since treating the efficiencies of generation units as constants would significantly simplify the calculation, only a simplified model is investigated in most research works. In this paper, aiming at optimizing the capacity configuration of an EH, we present a part-load characteristics-based (PLCB) model, in which the efficiencies of generation units will change with the fluctuating load. Based on the PLCB model, the accuracy of the EH model can be improved. Furthermore, a two-stage planning method is proposed to solve the optimal capacity configuration problem of the EH. Group Search Optimizer (GSO) is used to determine the optimal size in the first stage, and a mathematical programming method is applied to obtain the optimal operation of the EH in the second stage. Comparative studies using the PLCB model and the simplified model are performed to examine the impacts of equipment part-load characteristics on the sizing results. Simulation results indicate that the proposed model appears to have a better economic performance than the simplified model.