Analysis of basic conditions of the power grid interconnection among Xinjiang, Pakistan, and five Central Asian countries

Inter-regional and transnational grid interconnection is necessary for energy development. Xinjiang, which is rich in renewable energy resources, is adjacent to countries in Central Asia and has great potential for interconnection with its neighbors. This paper outlines China's relevant policies for transnational power interconnection, and introduces the energy structure, load demand endowments, and power supply status of Xinjiang, Pakistan, and five Central Asian countries. Further, it analyzes the advantages of the multinational power interconnection from the aspects of power supply and load complementation. Finally, from the perspective of technical support and practical basis, the feasibility of interconnection between Xinjiang, Pakistan, and five Central Asian countries have been analyzed. This paper provides a theoretical basis for promoting and implementing China's "Belt and Road" power transnational interconnected development strategy.

Two-stage stochastic-robust model for the self-scheduling problem of an aggregator participating in energy and reserve markets

This paper addresses a two-stage stochastic-robust model for the day-ahead self-scheduling problem of an aggrega-tor considering uncertainties.The aggregator,which integrates power and capacity of small-scale prosumers and flex-ible community-owned devices,trades electric energy in the day-ahead(DAM)and real-time energy markets(RTM),and trades reserve capacity and deployment in the reserve capacity(RCM)and reserve deployment markets(RDM).The ability of the aggregator providing reserve service is constrained by the regulations of reserve market rules,including minimum offer/bid size and minimum delivery duration.A combination approach of stochastic program-ming(SP)and robust optimization(RO)is used to model different kinds of uncertainties,including those of market price,power/demand and reserve deployment.The risk management of the aggregator is considered through con-ditional value at risk(CVaR)and fluctuation intervals of the uncertain parameters.Case studies numerically show the economic revenue and the energy-reserve schedule of the aggregator with participation in different markets,reserve regulations,and risk preferences.

Spatio-temporal Granularity Co-optimization Based Monthly Electricity Consumption Forecasting

Monthly electricity consumption forecasting(ECF)plays an important role in power system operation and electricity market trading.Widespread popularity of smart meters enables collection of fine-grained load data,which provides an opportunity for improvement of monthly ECF accuracy.In this letter,a spatio-temporal granularity co-optimization-based monthly ECF framework is proposed,which aims to find an optimal combination of temporal granularity and spatial clusters to improve monthly ECF accuracy.The framework is formulated as a nested bi-layer optimization problem.A grid search method combined with a greedy clustering method is proposed to solve the optimization problem.Superiority of the proposed method has been verified on a real smart meter dataset.