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The renewable-dominant hybrid generation systems (HGSs) are increasingly important to the electric power system worldwide. However, influenced by uncertain meteorological factors, the operational robustness of HGSs must be evaluated to inform the associated decision-making. Additionally, the main factors affecting the HGS's robustness should be urgently identified under deep uncertainties, as this provides valuable guidance for HGS capacity configuration. In this paper, a multivariate stochastic simulation method is developed and used to generate uncertain resource scenarios of runoff, photovoltaic power, and wind power. Subsequently, a long-term stochastic optimization model of the HGS is employed to derive the optimal operating rules. Finally, these operating rules are used to simulate the long-term operation of an HGS, and the results are used to evaluate the HGS's robustness and identify its main sensitivities. A clean energy base located in the Upper Yellow River Basin, China, is selected as a case study. The results show that the HGS achieves greater operational robustness than an individual hydropower system, and the robustness becomes weaker as the total capacity of photovoltaic and wind power increases. Additionally, the operational robustness of the HGS is found to be more sensitive to the total capacity than to the capacity ratio between photovoltaic and wind power. [ABSTRACT FROM AUTHOR]

Carbon peaking and carbon neutrality goals have proposed by many countries, including China. Meanwhile, China has also proposed the construction of a new-type electric power system with new energy as the main body, which will have great impacts on coal-fired power generation enterprises. The transformation of coal-fired power enterprises is imperative, and the evaluation of the organizational performance of coal-fired power enterprises is urgent, which can accurately determine the development status and even can find issues related to the transformation of coal-fired power enterprises. In this paper, a hybrid evaluation model is proposed based on the Variational Auto-Encoder algorithm and fuzzy comprehensive evaluation method improved by a vague set to comprehensively evaluate the organizational performance of coal-fired power enterprises. Eight coal-fired power enterprises in North China are selected for empirical analysis. The results show that the YC coal-fired power enterprise has the best organizational performance, while the NK coal-fired power enterprise has the worst organizational performance. Moreover, sensitivity and comparative analyses are carried out to verify the robustness of the evaluation result using the proposed hybrid method in this paper. This paper focuses on the organizational performance evaluation of coal-fired power enterprises, which can provide a reference for the smooth transformation and sustainable development of coal-fired power enterprises in the context of "Dual-Carbon" goals. [ABSTRACT FROM AUTHOR]

With the promotion of carbon peaking and carbon neutrality goals and the construction of renewable-dominated electric power systems, renewable energy will become the main power source of power systems in China. Therefore, ensuring frequency stability and system security will emerge as pivotal challenges in the future development process. Created by combining a Li-ion battery and a supercapacitor, a hybrid energy storage system (HESS), which possesses robust power regulation capabilities and rapid response capabilities, holds promise for supporting the frequency stability of power systems. In this context, the assessment of the economic viability of HESSs providing multitype frequency response services becomes a critical factor in their deployment and promotion. In this paper, an economic analysis approach for a Li-ion battery–supercapacitor HESS towards a multitype frequency response is presented. First, a multitype frequency response-oriented operational mode for the HESS is designed, outlining the roles and functions of the Li-ion battery and the supercapacitor in delivering distinct services. Moreover, building upon the analysis of the power trajectory of Li-ion batteries, a lifetime model for the HESS is proposed based on the rain-flow counting method. Furthermore, considering the competitive landscape for the HESS in the frequency regulation ancillary service market, a full lifecycle economic assessment model is proposed. Finally, case studies on actual power system frequency data and PJM market data are performed to verify the effectiveness of the proposed method, and the simulation results confirm that the HESS exhibits robust performance and a competitive advantage in providing multitype frequency response services. Additionally, it demonstrates commendable economic benefits, establishing its potential as a valuable contributor to frequency response services. [ABSTRACT FROM AUTHOR]

The paper introduces a robust methodology for evaluating complex electricity system efficiency, making several contributions. It systematically decomposes the electric power system into various processes, including power generation, transmission, distribution, and power consumption, with parallel sub-processes for production and domestic power consumption. Employing data envelopment analysis (DEA), the paper develops a network directional slacks-based (DSB) model to evaluate the whole system and the internal processes. The model is theoretically proven to be feasible, providing efficiency scores and identifying inefficiencies in the system and its processes. A noteworthy addition is the introduction of the DSB green Luenberger productivity index and its decomposition, offering an analysis of productivity changes in electricity systems. Empirical analysis covering 30 Chinese provinces from 2013 to 2019 uncovers the performance variations among provinces and processes, pointing out inefficiencies and changes in technology and efficiency over time. The empirical findings aim to support efforts in enhancing the overall efficiency of China's interprovincial power system. • We decompose the complex electric power system into four processes with dependence. • We build a network directional slacks-based (DSB) model for the complex power system. • We give a DSB green Luenberger productivity index (DPI) for our network model. • The efficiency score and DPI of the system are decomposed into those of processes. • We apply the model and indexes to electric power systems in Chinese provinces. [ABSTRACT FROM AUTHOR]

With the promotion of carbon peaking and carbon neutrality goals and the construction of renewable-dominated electric power systems, renewable energy will become the main power source of power systems in China. How to ensure the accommodation of renewable energy will also be the core issue in the future development process of renewable-dominated electric power systems. In this context, shared energy storage (SES), a novel business model combined with energy storage technologies and the sharing economy, has the potential to play an important role in renewable energy accommodation scenarios. This paper systematically organizes the application prospect, development status and key technologies of SES in the renewable energy accommodation scenario in the context of China, providing helpful references for the promotion of the business model. Firstly, a typical SES framework for renewable energy accommodation is described, and three basic forms of SES in this scenario are presented. Moreover, the application prospect of SES in the renewable energy accommodation scenario is quantitatively analyzed based on the renewable energy generation planning under the carbon peaking goal and the current guarantee mechanism of renewable energy accommodation. Furthermore, the rules for energy storage systems that provide the peak-regulation ancillary service in typical regions and provincial administrative regions in China are summarized, and the development status of SES in the renewable energy accommodation scenario is analyzed, combined with the actual market data. Finally, the key technologies to promote the further development of SES for renewable energy accommodation are presented. [ABSTRACT FROM AUTHOR]

We present a low‐altitude satellite survey of power line harmonic radiation (PLHR) at 50 Hz over Mainland China. We analyzed the month‐to‐month variation pattern in PLHR occurrence rate and further analyzed its correlation with some influencing factors (i.e., solar radiation, lightning flashes, and electricity consumption) using CSES satellite electric field data from 2019 to 2021. We also investigate the response of PLHR occurrence rate to COVID‐19. The statistical results show the dayside PLHR occurrence rate decreasing from winter to summer solstice and increasing from summer to winter solstice, which indicates it is controlled by the solar radiation. The nightside variation is more complex, which may be due to many sources that could influence the nightside lower ionosphere. The PLHR occurrence rate significantly decreased over Mainland China in February 2020, which is because of the significant decrease in electricity consumption due to the suspension of industrial production caused by COVID‐19. Plain Language Summary: Power line harmonic radiation (PLHR) is the electromagnetic waves radiated by electric power systems at harmonic frequencies of 50 or 60 Hz, depending on the frequency of the system on the ground. Previous research mainly focuses on identification of individual PLHR events and their subsequent analysis. However, the number of base‐frequency PLHR signal events is the most abundant, which is suitable for the statistical study of PLHR occurrence rate and its variation pattern, and further study of the factors affecting its variation pattern. In this paper, we use 3 years of electric field data from the China Seismo‐Electromagnetic Satellite (CSES) which is an LEO satellite launched into orbit in February 2018 to investigate the month‐to‐month variation pattern of PLHR occurrence rate over Mainland China and its correlation with the influencing factors. The response of PLHR occurrence rate to COVID‐19 are also investigated. Key Points: The month‐to‐month variation in power line harmonic radiation (PLHR) incidence is controlled by solar radiation and influenced by changes in electricity consumptionThe variation in nightside PLHR occurrence rate is complex, due to the nightside lower ionosphere being influenced by multiple sourcesThe PLHR occurrence rate over Mainland China decrease in February 2020 due to the decrease in electricity consumption caused by COVID‐19 [ABSTRACT FROM AUTHOR]

Under the background of renewable-dominated electric power system construction, the penetration rate of low-carbon and renewable distributed generation (DG) in distribution network is increasing, which has changed the form and operation mode of the distribution network. To deal with the output fluctuation of high penetration DG in the distribution network operations, it is necessary to evaluate the acceptance capacity of DG. The correct evaluation can realize the secure, economic and low-carbon configuration of DG. In this paper, an evaluation method of acceptance capacity of DG in the distribution network considering the carbon emission is proposed. Firstly, a multi-objective evaluation model of acceptance capacity of DG is constructed with the objectives of minimizing carbon emission in the full life cycle, minimizing node voltage deviation and maximizing line capacity margin. Secondly, the improved non-dominated sorting genetic algorithm II (NSGA-II) is employed to solve the model to determine the Pareto optimal solutions of DG configuration. Then, the comprehensive index of acceptance capacity evaluation is obtained based on entropy weight method to decide the optimal compromise solution. Finally, an actual 55-bus distribution network in China is used to verify the effectiveness of the proposed method. The simulation results show that the proposed evaluation method can comprehensively obtain the optimal compromise solution considering the reliability, economy and carbon emission benefits of distribution network operation, which guides the DG configuration in the distribution network. [ABSTRACT FROM AUTHOR]

Abstract The recent recurrence of wind curtailment in China underscores the rigidity and vulnerability of China's electricity regulatory regime. Using the theory of "fragmented authoritarianism", this paper identifies key institutional barriers causing wind curtailment in China and develops a framework for analyzing China's electricity regulatory regime. Specifically, the fragmented institutional authorities and responsibilities lead to departmentalism and coordination problems, both vertically and horizontally. The resulting mismatches in the investment, generation, and distribution of the electric power sector reflect direct causes for China's wind curtailment. This paper recommends that an electric power system reform with established regional spot markets is needed to fix China's wind curtailment problem. Highlights • Complex reasons lie behind serious wind curtailment in China. • Wind curtailment has its roots in the electricity regulation regime of China. • Fragmented authority causes coordination problems in the power sector. • Mismatches permeates the investment, generation and distribution of power. • A regional power spot market is needed to fix the wind curtailment problem. [ABSTRACT FROM AUTHOR]

Short‐term load forecasting is essential to power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate the intrinsic time‐related dimensions of electric load data and the decomposition methods into machine learning models so that their prediction accuracy and robustness still have much room for improvement. To solve this problem, this paper proposes a decomposition‐based multi‐time dimension long short‐term memory (DB‐MTD‐LSTM) model for short‐term electric load forecasting (STELF). In DB‐MTD‐LSTM, empirical mode decomposition with adaptive noise (CEEMDAN) is first introduced to smooth non‐linear non‐stationary electric load data and constrain the modal aliasing or noise of decomposed electric load data in the traditional decomposed method. A joint relevant time dimensions method (JRTDM) is then developed using autocorrelation analysis to rationally extract the temporal characteristics of decomposed data in multiple time dimensions. An improved LSTM called MTD‐LSTM is developed by combining JRTDM with LSTM, which can effectively apply multi‐dimensional time characteristics of the decomposed load to improve the predictive accuracy and robustness. Several datasets from Australia and China are performed to check the predictive performance of DB‐MTD‐LSTM. Experimental results verify that DB‐MTD‐LSTM has better predictive accuracy and satisfactory robustness compared with state‐of‐the‐art and conventional predictive models. [ABSTRACT FROM AUTHOR]

This paper investigates third party access regulatory issues in China's natural gas industry. We study the development of China's gas market-oriented reform and how third party access becomes a pressing issue in that context. This paper aims to report stakeholders' benefit and distributional effects during a hypothetical third party access process. To that end, we apply an oligopolistic equilibrium model, based on the mixed complementarity problem, to China's gas pipeline network. We compare two scenarios: a scenario without third party access and the other scenario where an independent pipeline operator optimizes flows. This latter scenario aims to guarantee that the maximum social benefit is achieved. In addition, the latter scenario transfers the congestion rent to former integrated gas companies to compensate their actual loss control of the pipeline operation, in order to minimize the adverse distributional effects for pipeline companies. The solution of the model indicates that operational separation is feasible with Pareto improvement in China's context. Moreover, it merits particular attention from policy makers in China that pipeline capacity scarcity should be properly evaluated and managed. [ABSTRACT FROM AUTHOR]

The volatility and uncertainty of wind energy has brought great challenges to its accommodation in electric power systems. To determine a wind power trading strategy in the real-time power market, the accurate assessment of wind energy accommodation capability (WAC) is of great significance. However, most current studies investigate the wind energy accommodation potential from the perspective of dispatch, but rarely provide quantitative assessment results. Additionally, the non-anticipativity of decisions has not been considered in the assessment yet. This paper proposes a novel quantitative assessment method for WAC based on the multi-stage robust optimization (RO), which addresses the anticipativity issues in the traditional two-stage method while maintaining the decision-making process' robustness. In the assessment method, the operational risk is integrated via a tractable scheme to obtain dynamic admissible WAC boundaries. To obtain the optimal solution of the proposed multi-stage RO problem, a fast robust dual dynamic programming (FRDDP) algorithm is employed, where the adaptive techniques are developed to accelerate the computation. Numerical studies on the modified IEEE 14-Bus system and a real-world system in Zhejiang Province of China validate the effectiveness of the proposed assessment model and adaptive acceleration techniques. The simulation results demonstrate the presented method brings a 18.87% reduction in operational cost, and reduces 29.34% curtailment of wind energy. Compared with the original FRDDP, the adaptive technique significantly reduces the computational consumption by 73.34% on the real-world test system. • A novel assessment method for WAC is proposed based on the multi-stage robust optimization. • Tractable risk evaluation method is integrated in the multi-stage framework. • Adaptive techniques are designed for FRDDP method to accelerate the optimal solution. • The proposed method achieves both non-anticipative and robust assessment decisions. [ABSTRACT FROM AUTHOR]

As the penetration rate of renewable energy increases in the electric power system, the issues of renewable power curtailment and system inertia shortage become more severe. Innovative solutions such as Cloud Energy Storage (CES) can be employed to address this challenge. However, the energy storage resources aggregated by the traditional CES business model mainly concentrate on Electrical Energy Storage (EES), which is still limited and expensive. It necessitates the exploration of new approaches to enhance the flexibility and cost-effectiveness of energy storage utilization, in which using District Heating System (DHS) as an equivalent energy storage resource of the power system is an effective method. Therefore, this paper proposes an optimal planning strategy of energy storage system under the CES model considering inertia support and electricity-heat coordination. Firstly, the system components and business model of the CES are described, and the framework of energy storage planning problem from the perspective of CES operator is formulated. Then the evaluation methods of energy storage utilization demand from CES users are proposed, including the evaluation of the renewable power curtailment, system minimum inertia requirement, and the equivalent energy storage ability of DHS. Based on this evaluation results, a bi-layer optimal energy storage planning model for the CES operator is established, where the upper-layer model determines the installed capacity of lithium (Li-ion) battery station and the lower-layer model determines the optimal schedules of the CES system. The numerical tests based on the operation profile of a typical city in China are carried out to demonstrate the effectiveness of the proposed method. The simulation result shows that the annual profit of the CES system can be improved by 15.26% after installing the energy storage system whose capacity is determined by the proposed method. • A CES model considering electricity-heat coordination and system inertia support is designed. • CES users' demand is evaluated by system operation simulation and inertia shortage estimation. • A bi-level optimization model of BES expansion planning is proposed for the CES operator. • Empirical study based on practical power system data in China to provide real-life experience. [ABSTRACT FROM AUTHOR]

Chinese electric power sector (EPS) contributes 52% of China's total CO 2 emissions. Given the severe global warming situation, carbon information disclosed by Chinese electric power enterprises (EPEs) has received extensive attention. Therefore, to understand the current level of carbon information disclosure (CID) in EPS and improve the CID quality, this paper evaluates the CID quality of public companies in the EPS. Based on the characteristics of the EPEs, this paper established a CID quality evaluation system from five aspects: integrity, relevance, reliability, comprehensibility, and comparability. An ANP-Cloud model is employed to evaluate the CID quality of 62 public companies in China's EPS. The analytic network process (ANP) method can quantify the correlation among evaluation indexes. The Cloud model achieves the transformation from qualitative evaluation to quantitative scoring with consideration of the uncertainty and fuzziness of the expert's evaluation language. The integration of two models, therefore, improves the comprehensiveness and objectiveness of the evaluation results. The evaluation results indicate that: (1) "Carbon emissions", "Carbon emission reduction targets" and "Independent chapter" indexes disclosed by the EPEs have a significant impact on CID quality. (2) The CID quality of the entire EPS public companies is low. The carbon information disclosed by EPEs cannot effectively represent the carbon management behavior of enterprises. (3) From the internal perspective of the EPS, the CID quality of thermal enterprises is relatively high, while hydropower enterprises are relatively low. Finally, this paper proposes viable suggestions to improve the CID quality from three aspects: EPEs, government, and public monitoring. • Constructed a carbon information disclosure (CID) quality evaluation system for electric power sector. • Integrated analytic network process (ANP) method and Cloud model to make the evaluation result reasonable. • Evaluation results show that the overall disclosure quality is low. • Indexes concerned by the carbon information users are determined. [ABSTRACT FROM AUTHOR]

The ongoing transformation of the world's energy system requires detailed power-system models that help plan a cost-effective and reliable integration of variable renewables and demand-side resources. The quality and depth of the results of these models depend on the existence of trustworthy, complete, and high-resolution data on extant electric power assets and the demand they serve, wind and solar resources, and projections on costs and performance of technologies that could be developed during the next three decades. This paper assesses the quality of China's power system's publicly available data compared to the U.S. It concludes that despite growing use of power system models to inform and analyze Chinese energy policy, the availability of necessary data is still a significant barrier that severely limits the transparency, replicability, relevance, and usefulness of their results. • Ongoing energy system transformation requires detailed power-system models. • Usefulness of models' results depends on reliable, complete high-resolution data. • Paper proposes criteria to assess the quality of publicly available data for models. • Publicly available data for China's electric power system is scant and low quality. • Filling data gaps will inform decisions for a sustainable electric power system. [ABSTRACT FROM AUTHOR]

The TD-LTE wireless private network for electric power systems is an important component of smart grids, and coverage analysis and interference identification are essential in operating and optimizing of power wireless private networks. This paper presents an approach to analyzing indoor and outdoor coverage scopes of cell radio signals and recognizing locations and sources of intra-network interference in the network deployed in a dense urban environment. This approach takes scenario modeling to represent terrain and on-ground objects given by digital maps, utilizes ray tracing to track the signal propagation trajectories, and calculates strength attenuation of radio signals due to signal propagation such as direct transmission, reflection, diffraction and refraction. The uniform grid is employed as the acceleration structure to speed up tracing signal propagation paths, and drive-testing measurement data and scenario-oriented propagation model calibration are used to improve analysis accuracy. Weak coverage spots and interference defect spots are defined and used to identify interference types and sources. We applied the approach to a tentative TD-LTE power wireless private network in a southern city in China, proving that the ray-tracing-based scheme is able to make precise analysis on coverage and interference in practical networks. [ABSTRACT FROM AUTHOR]

To address the global challenge of climate change and achieve sustainable development, decarbonizing the electric power system in China is considered the most imperative since the power sector is highly related to the decarbonization of other sectors. Significantly, China faces the dual challenges of rapidly growing electricity demand coupling with its commitment to of emission mitigation. This study examines the low-carbon transition and sustainable development of China's power system through 2035 using a multi-stage mixed-integer linear programming generation planning model by matching future demand growth trajectories with the least-cost power supply path. The electric power installed capacity, power generation, carbon emission, power-generating cost, and abatement potentials are analyzed through various scenarios. It is found that China's electricity demand will reach 9800–13300 Terawatt-hours in 2035 due to rapid electrification. Every 1 TWh increase in electricity demand requires 0.41–0.53 GW of newly installed capacity. Reducing the proportion of coal-fired power generation and replacing it with renewable energy is inevitable. However, for every 1 GW of coal power retired, 3.7 GW of renewable energy needs to be built. Incorporating electricity demand-side management is crucial to developing an effective decarbonization pathway for China's power sector, along with inclusive policies and technologies to meet China's requirements for dependable, affordable, and sustainable electricity. The results of this paper can provide a reference for policy makers in China as well as other emerging economies. • China's electricity consumption will continue to rise until 2035 and China will reach emission peak of power sector before 2030. • Meeting China's decarbonization requires gradually reducing coal-fired power capacity by up to the year 2020's 40.5% ty. • Every 1 GW of coal-power retirement requires the construction of 3.7 GW of renewable power capacity. • Demand-side management is crucial to easing high electricity demands for an effective power decarbonization trajectory in China. [ABSTRACT FROM AUTHOR]

Abstract Under the background of the structural adjustment of national energy strategic, ultra-high voltage (UHV) has been becoming an inevitable trend in the future development of the Chinese power grid. With the change of external environment, such as policy and economy, and the rapid development of the UHV, UHV project management is faced with great challenges and urgently need to establish a set of performance evaluation analysis tools. In this paper, we propose a slacks-based measure (SBM) data envelopment analysis (DEA) method for the performance evaluation and improvement of ultra-high voltage projects. Besides, we also build a set of performance evaluation index for UHV projects. Finally, we employ the preliminary planning UHV projects of a Chinese power grid company to illustrate the proposed method. Results show that our method is valid. Our method can provide an effective tool for decision support. [ABSTRACT FROM AUTHOR]

Socio-economic development is undergoing changes in China, such as the recently proposed carbon peak and carbon neutral targets, new infrastructure development strategy and the Coronavirus disease 2019 (COVID-19) pandemic. Meanwhile, the new-round marketization reform of the electricity industry has been ongoing in China since 2015. Therefore, it is urgent to evaluate the risk of electric power grid investment in China under new socio-economic development situation, which can help the investors manage risk and reduce risk loss. In this paper, a hybrid novel multi-criteria decision making (MCDM) method combining the latest group MCDM method, namely, Bayesian best–worst method (BBWM) and improved matter-element extension model (IMEEM) is proposed for risk evaluation of electric power grid investment in China under new socio-economic development situation. The BBWM is used for the weights' determination of electric power grid investment risk criteria, and the IMEEM is employed to rank risk grade of electric power grid investment. The risk evaluation index system of electric power grid investment is built, including economic, social, environmental, technical and marketable risks. The risk of electric power grid investment under new socio-economic development situation in Inner Mongolia Autonomous Region of China is empirically evaluated by using the proposed MCDM method, and the results indicate that it belongs to "Medium" grade, but closer to "High" grade. The main contributions of this paper include: (1) it proposes a hybrid novel MCDM method combining the BBWM and IMEEM for risk evaluation of electric power grid investment; and (2) it provides a new view for risk evaluation of electric power grid investment including economic, social, environmental, technical and marketable risks. The proposed hybrid novel MCDM method for the risk evaluation of electric power grid investment is effective and practical. [ABSTRACT FROM AUTHOR]

Greenhouse gas emission is a vital problem affecting the fate of mankind. To reduce the emission, passenger cars with various types of power system has been introduced in China. However, there is an uncertainty that whether these vehicles can reduce greenhouse gas emissions and how much emissions can be reduced, especially in terms of life cycle aspect. In this paper, a calculation method has been established to study their life cycle carbon emissions, with consideration of various power system, model class and electric driving range. It is found that most NEVs and Hybrid Electric Vehicle (HEVs) have emission reduction effects compared to internal combustion engine vehicles (ICEVs). The emission reduction rate of vehicles is between 6.56% and 44.4% in 2020, 13.97%–53.39% in 2025 and 19.65%–57.49% in 2030. It is also found that this reduction rate is significantly different between different vehicle class, power system and electric driving range. Battery electrical vehicle (BEVs) in 2020 with electric driving range over 700 km are even worse in carbon emission, the worst is −33.08%. The results could be useful for GHG emission prediction and power system selection for companies. Policies can be formulated to guide enterprise choosing power system with lower carbon emission. • List three main factors influencing the life cycle carbon emissions of passenger car. • Analyze the sensitivity of various vehicle models to three influencing factors. • Analyze the carbon reduction effect of three main factors between 2020 and 2030. [ABSTRACT FROM AUTHOR]

An important goal of China's electric power system reform is to create a double-side day-ahead wholesale electricity market in the future, where the suppliers (represented by GenCOs) and demanders (represented by DisCOs) compete simultaneously with each other in one market. Therefore, modeling and simulating the dynamic bidding process and the equilibrium in the double-side day-ahead electricity market scientifically is not only important to some developed countries, but also to China to provide a bidding decision-making tool to help GenCOs and DisCOs obtain more profits in market competition. Meanwhile, it can also provide an economic analysis tool to help government officials design the proper market mechanisms and policies. The traditional dynamic game model and table-based reinforcement learning algorithm have already been employed in the day-ahead electricity market modeling. However, those models are based on some assumptions, such as taking the probability distribution function of market clearing price (MCP) and each rival's bidding strategy as common knowledge (in dynamic game market models), and assuming the discrete state and action sets of every agent (in table-based reinforcement learning market models), which are no longer applicable in a realistic situation. In this paper, a modified reinforcement learning method, called gradient descent continuous Actor-Critic (GDCAC) algorithm was employed in the double-side day-ahead electricity market modeling and simulation. This algorithm can not only get rid of the abovementioned unrealistic assumptions, but also cope with the Markov decision-making process with continuous state and action sets just like the real electricity market. Meanwhile, the time complexity of our proposed model is only O(n). The simulation result of employing the proposed model in the double-side day-ahead electricity market shows the superiority of our approach in terms of participant's profit or social welfare compared with traditional reinforcement learning methods. [ABSTRACT FROM AUTHOR]

China is pursuing aggressive action to reduce greenhouse gas emissions from its coal-dominated electric power system. Two key strategies are power market reform and carbon pricing. This paper investigates the combined effects of these two strategies in reducing CO 2 emissions from power system operations. We develop an economic dispatch model to simulate hourly power supply system operation in China Southern Power Grid in 2018 under fifteen carbon pricing scenarios; these reflect a wide range of policy ambition, from today's carbon prices to much higher ones that aim to instigate aggressive emission mitigation. Our results show that moderate carbon pricing alone is insufficient to effectively reduce CO 2 emissions without concurrent power market reform. With power market reform and as carbon prices increase, large coal units supplemented by energy storage witness higher use rates as they supplant smaller coal-fired generators, until a carbon price of 300 RMB/TCO 2 phases out coal use in favor of natural gas. Only at carbon prices higher than 300 RMB/TCO 2 do emissions begin to decrease appreciably. Geographic disparities emerge among the five provinces that comprise the Southern Power Grid, with Guangdong witnessing the most CO 2 emission reduction at high carbon prices, while emission reductions in other provinces are negligible. Carbon pricing also dramatically increases total power system costs, even at low carbon prices. Our results show the necessity of introducing power market reform and carbon pricing policies concurrently if the goal is to reduce CO 2 emissions, with the ultimate goal being a carbon price significantly greater than 300 RMB/TCO 2. Because both options will be necessary, our research maps a path to deep emission reductions in China Southern Power Grid for investors, analysts, and policy makers as discussions regarding both reforms accelerate. • We assess combined effects of carbon pricing and power market reform in China. • Carbon pricing cannot reduce CO 2 emissions without power market reform. • CO 2 reductions from carbon pricing are negligible until prices of 300 RMB/TCO 2. • Even with low carbon prices, power supply system cost increase appreciably. • Existing system can only achieve shallow decarbonization, even at 1000RMB/TCO 2. [ABSTRACT FROM AUTHOR]

Micro-grids have been developed for over two decades as building blocks for future smart grids. Micro-grids have appeared with the advantages such as control flexibility, easy connection of renewable resources, high efficiency and immunity to large area blackouts. Similar to other countries, development of micro-grids in China has gone through from the early stage of AC microgrids to the current varieties of AC, DC and hybrid AC/DC micro-girds based on their applications. Many technical problems have been solved and new problems are continuously appeared during the development process. This paper presents a past, today and future for development of micro-grids in China. The current status of micro-grids and renewable energy sources in China is presented first. The topologies of the micro-grids in China are then introduced and classified into three types. Different control techniques of the micro-grids are introduced. Finally, technical challenges and future prospect of micro-grids in China are discussed. [ABSTRACT FROM AUTHOR]

Due to the uneven geographical distribution between load and wind power, currently it is difficult to integrate the large-scale wind power into the State Grid in China. There are significant wind curtailments for wind farms due to system restrictions. This paper proposes a new way of utilizing wind power, i.e., in-situ consumption, as an alternative to the costly development of long-distance transmission of large-scale wind power. Electrolytic aluminum load, which is one of the most typical high energy consuming loads, is employed to absorb the excess wind power in western China. An actual isolated industrial power system for aluminum production driven by wind power and coal-fired power is described. The penetration level of wind power in the isolated power system is up to 48.8%. The power imbalance between generation and load demand caused by wind power fluctuation or the tripping of coal-fired generators can dramatically impact the frequency stability of the isolated power system, which is of small inertia. An online identification method of power imbalance based on Wide Area Measurement System (WAMS) is presented. According to the characteristics of the electrolytic aluminum load, a system frequency control method by regulating the bus voltages of aluminum loads to eliminate the power imbalance is introduced. The simulation is done in Real Time Digital Simulator (RTDS) and the results verify the validity of the proposed frequency control method. [ABSTRACT FROM PUBLISHER]

Power system and its communication system, which can be called a cyber-physical system, are interconnected and interdependent on each other. This paper considers the interaction problem between power system and its communication module from the perspective of the topological structure. Firstly, some structural properties and centrality measures of complex networks are briefly reviewed. Furthermore, novel interactive measures are proposed to describe the interactive system in terms of topologies. Finally, based on these metrics, the statistical properties and the interactive relationships of the main power system and its communication module (abstracted as two complex heterogeneous networks) of one province in China are investigated. [ABSTRACT FROM AUTHOR]

Abstract: Under a market environment framework, ancillary services are an important guarantee of a power system׳s smooth operation. This paper presents a comprehensive discussion of the issues related to China׳s ancillary services in the electric power market. First, the status of ancillary service development in China is introduced, and then specific issues are analysed in depth. Finally, feasible policy recommendations for the issues are proposed. This study has important and practical implications for the development of ancillary services in China. [Copyright &y& Elsevier]

Accurate wind speed forecasting is increasingly essential for improving the operating efficiency of electric power systems. Numerous models have been proposed to obtain the accurate and stable wind speed forecasting results. However, previous proposed models are limited by single predictive model or cannot deal with complex nonlinear data characteristic, which resulted in poor and unstable prediction results. In this paper, a novel forecasting model that combines noise processing, statistical approaches, deep learning frameworks and multi-objective optimization algorithm is proposed. Multi-objective optimization algorithms can take advantage of the merits of benchmark prediction models to address nonlinear characteristics of wind speed series. The 10-min real wind speed data from three Sites in China are adopted for verifying the effectiveness of this proposed model. The experimental results of multi-step prediction show that the model achieves MAPE 1-step = 2.2109%, MAPE 2-step = 3.0309%, and MAPE 3-step = 4.2536% at Site 1; MAPE 1-step = 2.4586%, MAPE 2-step = 3.2034%, and MAPE 3-step = 4.6843% at Site 2; MAPE 1-step = 2.3180%, MAPE 2-step = 3.0846%, and MAPE 3-step = 4.4193% at Site 3. Therefore, the forecasting performance of this model is excellent, and it is beneficial to the dispatching and planning of power grid. • An advanced combined model is proposed for short-term wind speed prediction. • The latest data processing approach is applied to obtain stable sequences. • Two deep learning methods are involved in this forecasting system. • A modified multi-objective optimization is applied to find the optimal weights. [ABSTRACT FROM AUTHOR]

This paper proposed a model-based framework to optimize the energy-water-emissions nexus (EWEN) for regional electric power system (EPS) planning. The framework integrated the superiority of bi-level programming (BLP), two-stage stochastic programming (TSP), mix-integer programming (MIP), interval parameter programming (IPP) models and carbon emission trading (CET) mechanism, which could effectively deal with the planning problems in the EWEN optimization process. BLP helped decision makers make leader-follower choices when they have conflicting preferences. TSP with IPP could validly address the uncertain information expressed as interval values with unknown distribution. MIP was introduced as an efficacious tool to evaluate the capacity expansion potential in EPS. CET mechanism was applied to reinforce the CO 2 mitigation effect during the EWEN optimization process. Then the proposed framework was applied into a case study in Shanxi Province, a main thermal power base in China to optimize the EWEN. Results indicated that the power supply structure was inclined to show a low-carbon transition trend in the planning periods. Meanwhile, coupled with the strict water conservation policy, every 5% reduction in carbon emission density would lead to an average decreasing rate of 8% for SO 2 , 3% for NOx and 12% for PM. This study may help provide an effective way to deal with planning problems with conflicting and sequential goals under uncertainty and make better tradeoff strategies in EWEN optimization in EPS. • Propose a stochastic leader-follower framework for energy-water-emission nexus. • Control uncertainty by the combination of TSP and with IPP. • Interactions of CO 2 emissions and air pollution emissions are revealed. • Introduce CET mechanism to help improve the CO 2 mitigation effect. • The empirical results demonstrate the excellent performance of the framework. [ABSTRACT FROM AUTHOR]

Electric power is the foundation of the development of national economy, it is necessary to make a comprehensive evaluation of its development. Firstly, 17 secondary electric power development evaluation indexes are established from four aspects: electricity supply, electricity consumption, electrification level and carbon emission. Secondly, based on the cloud model, the subjective evaluation and analysis of the primary evaluation index for the national electric power development are realized. Then, the entropy method is used to objectively evaluate and analyze the secondary evaluation index of national electric power development. And the objective evaluation value of the primary evaluation index for the national electric power development is obtained. Thirdly, the TOPSIS method is used to integrate the subjective evaluation value obtained by the cloud model and the objective evaluation value obtained by the entropy method to comprehensively evaluate the status of national electricity development. Next, by collecting the electric power development evaluation index data of 11 typical countries, the electric power development of 11 countries was evaluated and analyzed by using the CM-EM-TOPSIS method proposed in this paper. From the results of the comprehensive evaluation of China 's electric power development, it is found that compared with other countries, the sensitivity of the queueing indicator value to the subjective evaluation weight is lower in China. Finally, according to the results of comprehensive evaluation and analysis of electric power development, the managerial implication is explored in China. • A comprehensive evaluation index system for national electric power development is established. • Cloud model is used to convert the subjective evaluation value of the primary evaluation index. • The secondary evaluation index of national electric power development is evaluated and analyzed by entropy method. • A national electric power development comprehensive evaluation method based on CM-EM-TOPSIS is proposed. • The current status of electricity power development in 11 typical countries is evaluated. [ABSTRACT FROM AUTHOR]

Yi, M.; Mo, L., and Shen, Q., 2020. Study on generation scheduling of cascade hydropower stations based on SAPSO. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 371–378. Coconut Creek (Florida), ISSN 0749-0208. With the reduction of energy and the reform of China's electric power system, it is of great help to develop clean energy such as hydropower to ease the lack of energy. Many methods are studied to solve the reservoir optimal scheduling problem, such as particle swarm optimization (PSO). In order to solve the premature problem and slow convergence of traditional PSO, simulated annealing particle swarm optimization (SAPSO) is proposed in this paper. The algorithm is improved by adding shrinkage factor and combining with simulated annealing algorithm. Establishing the maximum generation model, and SAPSO algorithm is used to optimize the power generation scheduling of four and ten hydropower stations. The case study shows that the application of SAPSO to solve the short-term optimal scheduling problem of cascade hydropower stations has achieved satisfactory results in effect and rationality. It provides a reliable method for the optimal operation of cascade hydropower stations. [ABSTRACT FROM AUTHOR]

China is accelerating the formulation of a renewable portfolio standard to promote the development and utilization of renewable energy. The implementation of the standard and corresponding tradable green certificates will change the scheduling procedures of thermal power and other renewable energy generators in the electric power system. This paper formulates a multi-objective dynamic economic emission dispatch model for wind-solar-hydro power under tradable green certificates. Weibull, Beta and Gumbel distributions are used to simulate the random behavior of wind, solar and hydro power, respectively, thus taking into account the overestimation and underestimation costs caused by the randomness of renewable energy. Moreover, a new multi-objective moth-flame optimization (MOMFO) based solution is proposed according to the characteristics of the dispatch model. The MOMFO is based on the external elite reservation and Pareto dominance. To improve the search capability, the strategy of average entropy initialization, Lévy flight and variable scale chaotic strategy are introduced. A heuristic dynamic relaxation method is proposed, which is used to handle the complex constraints of the model. Finally, the feasibility of the proposed scheduling model and the effectiveness of the algorithm are illustrated using examples of different scheduling situations based on IEEE 39-bus standard system. • A new dynamic environmental economic dispatching model is proposed for hybrid energy. • A multi-objective moth-flame optimization (MOMFO) is proposed for dispatching model. • MOMFO is effective and competitive compared with others multi-objective algorithms. • An efficient heuristic dynamic relaxation method is developed for complex constraints. • The influence of different trading systems on scheduling results in model is analyzed. [ABSTRACT FROM AUTHOR]

• Echo state network is used for point forecasting of wind power. • Wavelet transform is used to decompose the original signal into several sub-signals. • Ensemble technique is applied to reduce the uncertainty in wind power data. • The effectiveness of the proposed method is validated on two real wind farms. Accurate and reliable wind power forecasting is of great significance for the economic and safe operation of electric power and energy systems. However, due to the negative factors such as inaccurate numerical weather prediction and frequent ramping events, previous wind power prediction methods are difficult to meet actual needs for practical applications. To this end, this paper originally proposes a new wind power prediction method with high accuracy based on echo state network. This method is a hybrid of wavelet transform, echo state network and ensemble technique. Wavelet transform is used to decompose raw wind power time series data into different frequencies with better outliers and behaviors. Then, we adopt the echo state network to automatically learn the nonlinear mapping relationship between input and output in each frequency. Later, ensemble technique is applied to deal with the model misspecification and data noise problems, which are common in wind power prediction, thereby reducing the uncertainty of wind power forecasting and improving prediction accuracy. Finally, we use wind power data from real wind farms in Belgium and China to verify the feasibility and effectiveness of the proposed method. The simulation results show that the proposed method is superior to other benchmark algorithms in prediction accuracy, which indicates that the method has high potentials for practical application in real electric power and energy systems. [ABSTRACT FROM AUTHOR]