This page has only limited features, please log in for full access.
Many electricity markets around the world are still at developmental and transitional stages. To complete the transition and achieve the key objectives of perfect market design, designers often choose direct electricity procurement of large consumers (LCs) as a pilot. The trading mechanism is critical because it lays the foundation for the exploration of formulating a trading model and the succeeding solution; however, the existing trading mechanisms of direct electricity procurement struggle to cope with new challenges that electric power systems are facing. This paper proposes a novel two-stage trading mechanism, considering both the fairness and efficiency of direct electricity procurement. Based on the proposed trading mechanism, an agent-based trading model with multiple participants is developed. The simulation results of the transactions between LCs and generation companies (GenCos) illustrate the feasibility and effectiveness of the proposed mechanism. With this mechanism, LCs and GenCos will have more choices in the trading process and can benefit from the reduction of the average market price. The two-stage trading model provides a new choice for market designers and participants of direct electricity procurement.
Jian Zhang; Yanan Zheng; Mingtao Yao; Huiji Wang; Zhaoguang Hu. An Agent-Based Two-Stage Trading Model for Direct Electricity Procurement of Large Consumers. Sustainability 2019, 11, 5031 .
AMA StyleJian Zhang, Yanan Zheng, Mingtao Yao, Huiji Wang, Zhaoguang Hu. An Agent-Based Two-Stage Trading Model for Direct Electricity Procurement of Large Consumers. Sustainability. 2019; 11 (18):5031.
Chicago/Turabian StyleJian Zhang; Yanan Zheng; Mingtao Yao; Huiji Wang; Zhaoguang Hu. 2019. "An Agent-Based Two-Stage Trading Model for Direct Electricity Procurement of Large Consumers." Sustainability 11, no. 18: 5031.
Unlike existing studies focused on the causal relationship between electricity consumption and economic growth at the macro level, this paper uses monthly data from January 2006 to December 2015 and applies the correlation coefficient, as well as Kullback-Leibler (KL) divergence, to study the time difference relationship between sectoral electricity consumption and economic growth. The empirical results draw some main findings as follows: First, the time difference relationships show diversity at the sector level but will form a kind of overall characteristic between economic growth and total electricity consumption. Secondly, not all sectors have a remarkable correlation between sectoral electricity consumption and economic growth as only part of them have reasonable values to describe the time difference relationship. Thirdly, the results present both diversity and aggregation at the industry level, while lagging sectors mainly concentrate in the manufacturing industry. The relationship between sectoral electricity consumption and economic growth can be further explored and described from a new perspective based on the results. Further, the trend of economic development and sectoral electricity consumption can be predicted to help policy-makers formulate proper policies.
Jian Zhang; Zhaoguang Hu; Yanan Zheng; Yuhui Zhou; Ziwei Wan. Sectoral Electricity Consumption and Economic Growth: The Time Difference Case of China, 2006–2015. Energies 2017, 10, 249 .
AMA StyleJian Zhang, Zhaoguang Hu, Yanan Zheng, Yuhui Zhou, Ziwei Wan. Sectoral Electricity Consumption and Economic Growth: The Time Difference Case of China, 2006–2015. Energies. 2017; 10 (2):249.
Chicago/Turabian StyleJian Zhang; Zhaoguang Hu; Yanan Zheng; Yuhui Zhou; Ziwei Wan. 2017. "Sectoral Electricity Consumption and Economic Growth: The Time Difference Case of China, 2006–2015." Energies 10, no. 2: 249.
Electricity demand and economic growth are closely correlated. Electricity is an important means of production and subsistence and plays an important role in the national economy system. Accurate electricity demand forecasting results could provide the basis for the power grid planning and construction and therefore has important social and economic benefits. In this paper, a long-term electricity demand forecasting model that contains six kinds of Agent is proposed based on multi-agent technology. The model is validated by the electricity consumption data of 2011-2014. Then the industry-wide electricity demand forecasting results from 2015 to 2025 are obtained. Through case study, the results change affected by economic policy is studied. The results show that the electricity demand will increase under loose monetary policy.
Zhang Jian; Hu Zhao-Guang; Zhou Yu-Hui; Duan Wei. Long Term Electricity Demand Forecasting with Multi-agent-Based Model. Transactions on Petri Nets and Other Models of Concurrency XV 2015, 578 -585.
AMA StyleZhang Jian, Hu Zhao-Guang, Zhou Yu-Hui, Duan Wei. Long Term Electricity Demand Forecasting with Multi-agent-Based Model. Transactions on Petri Nets and Other Models of Concurrency XV. 2015; ():578-585.
Chicago/Turabian StyleZhang Jian; Hu Zhao-Guang; Zhou Yu-Hui; Duan Wei. 2015. "Long Term Electricity Demand Forecasting with Multi-agent-Based Model." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 578-585.