Interconnected power systems that link several countries and fully utilize their individual resources in a complementary manner are becoming increasingly important.As these systems enhance accommodation of renewable energy,they also represent a move toward low-carbon and low-emission power systems.In this paper,a low-carbon dispatch model is proposed to coordinate the generation output between several countries where the carbon emission constraint is a priority.An adjustable robust optimization approach is used to find the optimal solution under the worst-case scenario to address the uncertainties associated with renewable energy resources.A specific constraint is that the area control error for each country should be self-balanced.Furthermore,a reformation using participation factors is presented to simplify the proposed robust dispatch model.Simulation results for practical interconnected power systems in northeast Asian countries verify the effectiveness of the proposed model.

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Congo River has abundant hydropower resources,and large-scale cascade power stations,such as the Grand Inga,can be constructed in downstream locations.However,the fragile economic foundations of the Democratic Republic of the Congo and neighboring Central African countries,and the small-scale regional power consumption market prohibit the implementation of large-scale hydropower projects.As the high-voltage,long-distance power transmission technology matures,hydropower from the Grand Inga can be delivered to load centers in other regions of Africa.This study establishes a 6 dimensional comprehensive assessment model using the best-worst method to evaluate large-scale,long-distance,cross-border power interconnection projects.The model is applied to evaluate all the candidate inter-regional power delivery schemes of the Inga III hydropower station,and the evaluation results can effectively help investment institutions and policy makers in policy making and potential market targeting.

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With the global economy integration and progress in energy transformation,it has become a general trend to surpass national boundaries to achieve wider and optimal energy resource allocations.Consequently,there is a critical need to adopt scientific approaches in assessing cross-border power grid interconnection projects.First,considering the promotion of large-scale renewable energy resources and improvements in system adequacy,a comprehensive assessment index system,including costs,socio-economic benefits,environmental benefits,and technical benefits,is established in this study.Second,a synthetic assessment framework is proposed for cross-border power grid interconnection projects based on the index system comprising cost-benefit analysis,with market and network simulations,iterative methods for indicator weight evaluation,and technique for order preference by similarity to an ideal solution (TOPSIS) method for the project rankings.Finally,by assessing and comparing three cross-border projects between Europe and Asia,the proposed index system and assessment framework have been proved to be effective and feasible;the results of this system can thus support investment decision-making related to such projects in the future.

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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.

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To ensure the safety and stability of power grids with photovoltaic (PV) generation integration,it is necessary to predict the output performance of PV modules under varying operating conditions.In this paper,an improved artificial neural network (ANN) method is proposed to predict the electrical characteristics of a PV module by combining several neural networks under different environmental conditions.To study the dependence of the output performance on the solar irradiance and temperature,the proposed neural network model is composed of four neural networks,it called multineural network (MANN).Each neural network consists of three layers,in which the input is solar radiation,and the module temperature and output are five physical parameters of the single diode model.The experimental data were divided into four groups and used for training the neural networks.The electrical properties of PV modules,including I-V curves,PV curves,and normalized root mean square error,were obtained and discussed.The effectiveness and accuracy of this method is verified by the experimental data for different types of PV modules.Compared with the traditional single-ANN(SANN) method,the proposed method shows better accuracy under different operating conditions.

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Owing to the rapid development of microgrids (MGs) and growing applications of renewable energy resources,multiobjective optimal dispatch of MGs need to be studied in detail.In this study,a multiobjective optimal dispatch model is developed for a standalone MG composed of wind turbines,photovoltaics,diesel engine unit,load,and battery energy storage system.The economic cost,environmental concerns,and power supply consistency are expressed via subobjectives with varying priorities.Then,the analytic hierarchy process algorithm is employed to reasonably specify the weight coefficients of the subobjectives.The quantum particle swarm optimization algorithm is thereafter employed as a solution to achieve optimal dispatch of the MG.Finally,the validity of the proposed model and solution methodology are confirmed by case studies.This study provides reference for mathematical model of multiojective optimization of MG and can be widely used in current research field.

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Predicting wind power generation over the medium and long term is helpful for dispatching departments,as it aids in constructing generation plans and electricity market transactions.This study presents a monthly wind power generation forecasting method based on a climate model and long short-term memory (LSTM) neural network.A nonlinear mapping model is established between the meteorological elements and wind power monthly utilization hours.After considering the meteorological data (as predicted for the future) and new installed capacity planning,the monthly wind power generation forecast results are output.A case study shows the effectiveness of the prediction method.

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Due to the shortage of fossil energy and the pollution caused by combustion of fossil fuels,the proportion of renewable energy in power systems is gradually increasing across the world.Accordingly,the capacity of power systems to accommodate renewable energy must be improved.However,integration of a large amount of renewable energy into power grids may result in network congestion.Hence,in this study,optimal transmission switching (OTS) is considered as an important method of accommodating renewable energy.It is incorporated into the operation of a power grid along with deep peak regulation of thermal power units,forming an interactive mode of coordinated operation of source and network.A stochastic unit commitment model considering deep peak regulation and OTS is established,and the role of OTS in promoting the accommodation of renewable energy is analyzed quantitatively.The results of case studies involving the IEEE 30-bus system demonstrate that OTS can enable utilization of the potential of deep peak regulation and facilitate the accommodation of renewable energy.

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Distribution networks in China and several other countries are predominantly neutral inefficiently grounding systems (NIGSs),and more than 80% of the faults in distribution networks are single-phase-to-ground (SPG) faults.Because of the weak fault current and imperfect monitoring equipment configurations,methods used to determine the faulty line sections with SPG faults in NIGSs are ineffective.The development and application of distribution-level phasor measurement units (PMUs) provide further comprehensive fault information for fault diagnosis in a distribution network.When an SPG fault occurs,the transient energy of the faulted line section tends to be higher than the sum of the transient energies of other line sections.In this regard,transient energy-based fault location algorithms appear to be a promising resolution.In this study,a field test plan was designed and implemented for a 10 kV distribution network.The test results demonstrate the effectiveness of the transient energy-based SPG location method in practical distribution networks.

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For the implementation of power market in China,medium- and long-term security checks are essential for bilateral transactions,of which the electricity quantity that constitutes the generation feasible region (GFR) is the target.However,uncertainties from load forecasting errors and transmission contingencies are threats to medium- and long-term electricity trading in terms of their influences on the GFR.In this paper,we present a graphic distortion pattern in a typical threegenerator system using the Monte Carlo method and projection theory based on security constrained economic dispatch.The underlying potential risk to GFR from uncertainties is clearly visualized,and their impact characteristics are discussed.A case study on detailed GFR distortion was included to demonstrate the effectiveness of this visualization model.The result implies that a small uncertainty could distort the GFR to a remarkable extent and that different line-contingency precipitates disparate the GFR distortion patterns,thereby eliciting great emphasis on load forecasting and line reliability in electricity transactions.

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