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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]

Summary: Distributed generation (DG) technologies are environmentally friendly and have low operating costs, and thus, distributed generators are widely used for the energy supplies of buildings. Solar energy used for on‐demand heating of buildings is also a mature technology that is environmentally friendly and inexpensive and has a short recycling life cycle. This paper proposes a high proportion of new energy access–combined cooling, heating, and power (HPNE‐CCHP) system composed of a distributed generator and solar energy heat pump system. To obtain the optimum capacity of the HPNE‐CCHP system, nondominated sorting genetic algorithm‐II (NSGA‐II) was used to optimize the system by considering the life cycle cost (LCC) and life cycle pollutant emissions (LCPE) as the objective functions. A mixed integer economic scheduling model (MIESM) was proposed to make the HPNE‐CCHP system operate more economically. Finally, an HPNE‐CCHP system was constructed for a building in Northern China. The simulation results show that an HPNE‐CCHP system with a moderate proportion of new energy is more economical and environmentally friendly than a traditional CCHP system. Building occupants, depending on their desired spending, can select the best capacity configuration on the Pareto frontier. Although pollutant emissions will be reduced as the proportion of new energy increases, this type of configuration is expensive. [ABSTRACT FROM AUTHOR]

Power system planning is essential for ensuring development of a safe and stable power system. Articles on power system planning have been published in recent decades. In this paper, bibliometric analysis is applied to power system planning to identify its basic characteristics, and to summarize the research hotspots and trends. A total of 4562 publications were obtained from the Web of Science during 1971–2020. The 4562 publications cover 8136 keywords, 1526 journals, 107 countries/territories, and 2519 institutions. IEEE Transactions on Power Systems is the key journal in power system planning; USA, China, and England are the countries that dominate publication production. Research hotspots and trends were analyzed using author-keyword co-occurrence analysis and keyword burst detection. Four clusters were identified and topics from Cluster I (central nodes as power system stability and transient stability) and Cluster IV (central nodes as power system simulation and economics) are important for power system planning research. However, their relative popularities have declined over the past two decades. This indicates that Cluster II (central nodes as renewable energy, wind power, and energy storage) and Cluster III (central nodes as distributed generation and smart grids) will be the focus of future research. [ABSTRACT FROM AUTHOR]

The increasing penetration of distributed generation (DG) brings about great fluctuation and uncertainty in distribution networks. In order to improve the ability of distribution networks to cope with disturbances caused by uncertainties and to evaluate the maximum accommodation capacity of DG, a multi-objective programming method for evaluation of the accommodation capacity of distribution networks for DG is proposed, considering the flexibility of distribution networks in this paper. Firstly, a multi-objective optimization model for determining the maximum accommodation of DG by considering the flexibility of distribution networks is constructed, aiming at maximizing the daily energy consumption, minimizing the voltage amplitude deviation, and maximizing the line capacity margin. Secondly, the comprehensive learning particle swarm optimization (CLPSO) algorithm is used to solve the multi-objective optimization model. Then, the mixed strategy Nash equilibrium is introduced to obtain the frontier solution with the optimal joint equilibrium value in the Pareto solution set. Finally, the effectiveness of the proposed method is demonstrated with an actual distribution network in China. The simulation results show that the proposed planning method can effectively find the Pareto optimal solution set by considering multiple objectives, and can obtain the optimal equilibrium solution for DG accommodation capacity and distribution network flexibility. [ABSTRACT FROM AUTHOR]

Smart Grid presages an advanced power grid that revolutionizes the century-old traditional power grid and the way mankind uses energy. In China, the pressure on the current grid exerted by growing demand, environmental issues and the unbalanced energy use structure makes the transition to a ‘smarter’ and ‘cleaner’ grid inevitable. This paper firstly contrasts the concepts and research priorities of Smart Grid of China and other developed countries; then turns to the situation of Chinese energy and power use. China has the largest generating capacity, 79% of which is coal-fired plants. And China is also the largest carbon emitter in the world. Despite the challenges, China is also the most promising market for Smart Grid. The components of Smart Grid, especially the development of renewable energy, electric vehicles and smart substation are reinforced in the Chinese 12th Five-Year Plan (2011–2015). The paper examines also efforts by government, power utilities and research institutes. The paper concludes that developing Smart Grid will be beneficial both to China and the world. [ABSTRACT FROM PUBLISHER]

Electric power industry is a key to achieve the low-carbon social and economic development. Distributed generation (DG) has great potential in the development of low-carbon power grid because of its non-polluting property and large reserves. Therefore, it is of great importance to have a comprehensive investigation on low-carbon impact of DG on power grids. In this paper, regional power grids are focused as they are crucial supporters of regional economic and social activities and the primary undertakers of low-carbon development. Specifically, first, the carbon dioxide emission coefficient and energy consumption of DG are evaluated from the aspect of its life cycle. Then, the wind farm and photovoltaic power station are compared in terms of carbon dioxide emissions and energy consumption, indicated that wind power is superior in reducing the carbon dioxide emissions. Finally, the carbon intensity of provincial power grid is evaluated by grey prediction model with historical data such as electricity consumption, gross domestic product (GDP), carbon dioxide emission coefficient. Taking Jiangxi provincial power grid as the example, the results demonstrate that the proposed prediction method is of high accuracy and the integration of DGs can effectively reduce carbon dioxide emissions and carbon intensity, thus, DG should be widely integrated into provincial power grids. [ABSTRACT FROM AUTHOR]

• The analytical expression of failure rates is derived. • The penalty cost of power shortages can be expressed as an explicit function. • A bi-level coordinated configuration programming model is built. • The bi-level model is transformed to a mixed-integer nonlinear programming problem. Improving system reliability and voltage profiles in distribution networks requires the simultaneous installation of distributed generations and automation devices. This paper builds a bi-level programming model for the coordinated configuration of distributed generations and automation devices. The upper-level problem is to minimize the total investment cost of distributed generations and automation devices and the penalty cost of power shortages, and the lower-level problem is to minimize the voltage deviation during typical operation mode. To explicitly represent the relationship between the penalty cost of power shortages and installation locations of distributed generations and automation devices, an analytical expression of the failure rates of load nodes with respect to installation locations of distributed generations and automation devices is derived. The bi-level model can be transformed to a mixed-integer nonlinear programming problem by applying the Karush–Kuhn–Tucker theorem because the lower-level problem is a quadratically constrained linear programming problem. Case studies on an IEEE 33-bus distribution system and a practical 110-bus distribution system in China are used to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]