Optimization of Electricity Purchase and Sales Strategies of Electricity Retailers under the Condition of Limited Clean Energy Consumption

In the process of my country’s energy transition,the clean energy of hydropower,wind power and photovoltaic power generation has ushered in great development,but due to the randomness and volatility of its output,it has caused a certain waste of clean energy power generation resources.Regarding the purchase and sale of electricity by electricity retailers under the condition of limited clean energy consumption,this paper establishes a quantitative model of clean energy restricted electricity fromthe perspective of power system supply and demand balance.Then it analyzes the source-charge dual uncertain factors in the electricity retailer purchasing and selling scenarios in the mid-to long-term electricity market and the day-ahead market.Through the multi-scenario analysis method,the uncertain clean energy consumption and the user’s power demand are combined to form the electricity retailer’s electricity purchase and sales scene,and the typical scene is obtained by using the hierarchical clustering algorithm.This paper establishes a electricity retailer’s risk decisionmodel for purchasing and selling electricity in themid-and long-term market and reduce-abandonment market,and takes the maximum profit expectation of the electricity retailer frompurchasing and selling electricity as the objective function.At the same time,in themediumand longterm electricity market and the day-ahead market,the electricity retailer’s purchase cost,electricity sales income,deviation assessment cost and electricity purchase and sale risk are considered.The molecular results show that electricity retailers can obtain considerable profits in the reduce-abandonment market by optimizing their own electricity purchase and sales strategies,on the premise of balancing profits and risks.

Data Mining Based Integrated Electric-Gas Energy System Multi-Objective Optimization

With the proposal of carbon neutrality,how to improve the proportion of clean energy in energy consumption and reduce carbon dioxide emissions has become the important challenge for the traditional energy industry.Based on the idea of multi-energy complementarity,a typical integrated energy system consisting of electric system and gas system is constructed based on the application of power to gas(P2G)technology and gas turbine in this paper.Furthermore,a multi-objective optimization model with economic improvement,carbon emission reduction and peak-load shifting as objectives is proposed,and solved by BSO algorithm.Finally,a typical power-gas coupling system is selected as an example to verify the effectiveness of the model.The results showed that the proposed multi-objective optimization model based on BSO algorithm can better play the complementary characteristics of the electric and gas system,and significantly improve the comprehensive benefits of system operation.

Clean Energy Consumption of Power Systems Towards Smart Agriculture: Roadmap, Bottlenecks and Technologies

Over the past decades,both agriculture and power systems have faced serious problems,such as the power supply shortage in agriculture,and difficulties of clean energy consump-tion in the power system.To address and overcome these issues,this paper proposes an idea to combine smart agriculture and clean energy consumption,use surplus clean energy to supply agriculture production,and utilize smart agriculture to support power system with clean energy penetration.A comprehensive review has been conducted to first depict the roadmap of coupling a agriculture-clean energy system,analyze their feasibilities and advantages.The recent technologies and bottlenecks are summa-rized and evaluated for the development of a combined system consisting of smart agriculture production and clean energy consumption.Several case studies are introduced to explore the mutual benefits of agriculture-clean energy systems in both the energy and food industries.

ATwo-LayerOptimal Scheduling Strategy forRural Microgrids Accounting for Flexible Loads

In the context of China’s“double carbon”goals and rural revitalization strategy,the energy transition promotes the large-scale integration of distributed renewable energy into rural power grids.Considering the operational characteristics of rural microgrids and their impact on users,this paper establishes a two-layer scheduling model incorporating flexible loads.The upper-layer aims to minimize the comprehensive operating cost of the rural microgrid,while the lower-layer aims to minimize the total electricity cost for rural users.An Improved Adaptive Genetic Algorithm(IAGA)is proposed to solve the model.Results show that the two-layer scheduling model with flexible loads can effectively smooth load fluctuations,enhance microgrid stability,increase clean energy consumption,and balance microgrid operating costs with user benefits.