Review on Reactive Power and Voltage Optimization of Active Distribution Network with Renewable Distributed Generation and Time-Varying Loads

With a high proportion of renewable distributed generation and time-varying load connected to the distribution network, great challenges have appeared in the reactive power optimization control of the active distribution networks. This paper first introduces the characteristics of active distribution networks, the mechanism and research status of wind power, photovoltaic, and other renewable distributed generators, and time-varying loads participating in reactive power and voltage optimization. Then, the paper summarizes the methods of reactive power optimization and voltage regulation of active distribution network, including multi-timescale voltage optimization, coordinated optimization of network reconfiguration and reactive power optimization, coordinated optimization of active and reactive power optimization based on model predictive control, hierarchical and zoning control of reactive power, and voltage and power electronic switch voltage regulation. The pros and cons of the reactive power optimization algorithms mentioned above are summarized. Finally, combined with the development trend of the energy Internet, the future directions of reactive power and voltage control technology in the active distribution network are discussed.

Configuration method of BESS in the wind farm and photovoltaic plant considering active and reactive power coordinated optimization

To promote the coordinated development between renewable energy and the distribution network, a capacity allocation model of battery energy storage systems (BESS) is proposed to achieve the coordinated optimization for active and reactive power flow, which can reduce the voltage deviation and improve the absorptive capacity for renewable energy. In addition, BESS with four-quadrant operation characteristics, on-load tap changer, and capacitor banks are treated as flexible devices to improve the adaptability for renewable energy fluctuations. In view of the uncertainties of renewable energy caused by the inaccuracy of historical sample data, a set of extreme scenarios with the characteristics of temporal and spatial correlation are considered to obtain a robust BESS configuration decision. The big-M approach and the second-order conic relaxation technique are utilized to convert the BESS capacity allocation model into a mixed-integer linear programming problem. Finally, the IEEE 33-node distribution system is taken as an example to verify the effectiveness of the proposed method.

Rational Reflection in the Post-epidemic Era: Institutional Advantages and Improvement of Governance Efficiency

Currently, the national epidemic prevention and control is still facing the challenges of secondary risk stacking, coordinated optimization of multi-party governance resources, and effective conversion of normal and abnormal conditions. Therefore, systematic thinking should be established in the normalized precision prevention and control and local emergency response, prevention and control should be implemented in accordance with the law, and source governance should be strengthened so as to overcome the risk challenges and improve governance efficiency.

Data-Driven Real-Time Pricing Strategy and Coordinated Optimization of Economic Load Dispatch in Electricity Market

Compared to the step tariff, the real-time pricing (RTP) could be more stimulated for household consumers to change their electricity consumption behaviors. It can reduce the reserve capacity, peak load, and of course the electricity bill, which could achieve the purpose of saving energy. This paper proposes a coordinated optimization algorithm and data-driven RTP strategy in electricity market. First, the electricity price is divided into two parts, basic electricity price and fluctuating price. When the electricity consumption is equal to the average daily electricity consumption, the price is defined as the basic electricity price, which is the clearing electricity price. The consumer electricity data are analyzed. A random forest algorithm is adopted to predict the load data. Optimal adjustment parameters are obtained and the load fluctuation and the fluctuation of the electricity price are further quantified. Secondly, the appliances are modeled. The operation priority is established based on the preferences of customers and the Monte Carlo method is used to form the power load curve. Then, the smart energy planning unit is proposed to optimize the appliances on/off time and running time of residential electrical appliances. An incentive mechanism is used to further standardize the temporary electricity consumption. An improved multiobjective particle swarm optimization (IMOPSO) algorithm is adopted, which adopts the linear weighted evaluation function method to maximize the consumer’s social welfare while minimizing the electricity bill. The simulation proves that the stability of the power grid is improved while obtaining the best power strategy.

A Coordinated Optimization Model of the Complex System of the Green Supply Chain Distribution Network

A well-established distribution network is fundamental to the sound management of the green supply chain. To adapt to the market demand and policies for green products, it is urgent to build an efficient and rational logistic distribution network for the green supply chain. Many researchers have tried to design distribution networks through the coordinated optimization of the green supply chain, in the light of realistic situation. However, there are very few optimization models that consider all kinds of influencing factors. To solve the problem, this paper attempts to establish a coordinated optimization model of the complex system of the green supply chain distribution network (GSCDN). Firstly, the authors plotted the structure and game logic of the GSCDN and defined the upper limit of sales induced by the limited production capacity of producers. Secondly, the coordinated optimization conditions were configured for the distributor layer, producer layer, and market demand layer, and a coordinated optimization model was set up for the complex system. Finally, the contractual coordinated optimization mechanism was detailed for the complex system under the profit-sharing contract. The proposed model and solving algorithm were proved valid through experiments.