تجزیه و تحلیل هزینه یک سیستم قدرت با استفاده از جریان برق بهینه احتمالاتی با ادغام ذخیره سازی انرژی و تولید باد

This paper examines the storage application and its optimal placement for the social cost and transmission congestion relief of wind integration. Probability density functions (PDFs) are used to characterize the uncertainties of wind speed and load. A probabilistic optimal power flow (POPF) is developed using two-point estimation which incorporates the storage system either as a variable load or as a variable generator. Storage systems are optimally placed and adequately sized using a particle swarm optimization (PSO) to minimize the sum of operation and congestion costs over a scheduling period. A technical assessment framework is developed to enhance the efficiency of wind integration and evaluate the economics of storage technologies and conventional gas-fired alternatives. The proposed method is used to carry out a cost-benefit analysis for the IEEE 24-bus system and determine the most economical technology. Optimal storage distribution and its potential to relieve the transmission congestion are evaluated for higher wind penetration levels.

Recent developments in advanced energy storage technologies combined with the associated technical, economic and environmental benefits provide energy storage systems with a broad range of potential to optimize grid connected wind power resources [1]. Integration of wind generation with more than 20% penetration levels requires additional regulation and spinning reserve resources for grid stability purposes. These services incur some costs which have been the subject of several investigations in the US and Europe [2], [3], [4], [5] and [6]. Increasing amounts of these costs with wind penetration levels gives an opportunity for energy storage systems to provide all or some portion of these ancillary services. Rated capacity of the wind power is the determining factor in calculating the amount of grid capacity required to accommodate the full wind power resource. However, average capacity of wind power is typically between 30% and 40% of rated capacity. This is due to the intermittent nature of wind power which makes it a variable and uncertain energy resource. Therefore, when compared with conventional generating technologies, more transmission capacity per unit of delivered wind energy is assigned to deal with wind power intermittency [1]. Wind power may be curtailed during high wind periods to avoid transmission congestion. This may impose an extra cost to the grid operators or a loss of revenue to the wind generators. Energy storage can be used to store the wind energy in excess of transmission capacity and dispatch it later when transmission capacity is available. Effective utilization of transmission capacity could be realized by optimizing the placement and scheduling of energy storage. This results in transmission congestion relief and/or transmission expansion deferral [7]. Adequate sizing of energy storage is also required to efficiently integrate renewable resources and justify the cost of storage deployment over the more conventional alternatives [8]. Therefore, application of large-scale energy storage for renewable integration calls for a techno-economic assessment framework to enhance grid operability and reduce operation cost [9], [10] and [11]. This is particularly essential for transmission congestion relief application whose lack of operational practices limits the knowledge about operating, siting, sizing, and optimal scheduling of energy storage technologies in power systems with renewable energy sources. This has been the subject of investigation in few publications [12] and [13]. Wind uncertainties are not considered in [12], which questions the applicability of the proposed methodology for real world problems. In addition, the compressed air energy storage (CAES) is arbitrarily placed close to the wind resource and/or load center, with no attempt at optimizing its location and size to minimize congestion-related costs. Ref. [13] concludes with installing storage systems at locations that are downstream from the point of congestion in a transmission system. This would allow for the transmission of energy for charging when there is no congestion. The stored energy can be later discharged to reduce transmission capacity requirements during peak load periods. However, this conclusion cannot be generalized for a transmission network where the presence of several transmission lines and load centers complicates the optimal placing problem. This paper proposes a POPF with energy storage integration and wind generation. The proposed methodology uses a PSO approach together with a two-point estimation to examine the storage applications for social cost and transmission congestion relief. The storage system is incorporated into the POPF model to store the extra wind power that would otherwise be curtailed. An economic assessment framework is also developed to evaluate the economic advantage of storage technologies over more conventional alternatives. Section 2 explains the PSO and two-point estimation methods. It also presents probabilistic models of wind and load based on actual data. In addition, economic characteristics of storage technologies and gas-fired generators are discussed in this section. Section 3 investigates different case studies and conclusions are presented in Section 4.

A technical framework is proposed to evaluate the energy storage application, its optimal placement and economic advantage for the social cost and transmission congestion relief of wind integration with higher penetration levels. Wind generation and load are stochastically modeled using historical data and curve fitting. The storage system is incorporated into the POPF model to store the extra wind power that would otherwise be curtailed. A particle swarm optimization is proposed to optimally place and adequately size the energy storage for minimizing the sum of operation and congestion costs over a scheduling period. The proposed method was tested on the IEEE 24-bus system under two scenarios with different wind penetration levels. Simulation results have demonstrated the advantage of co-locating wind and storage for its social cost relief application. This advantage was due to the decreased operation cost of the system for lower wind penetrations where the transmission congestion was not significant. For higher wind penetrations, the storage system was placed at a bus other than the wind bus which maintained a compromise between the operation cost increment and congestion cost decrement to realize the best optimal solution. Optimal storage distribution and its application for transmission congestion relief utilized the transmission capacity more effectively and increased the efficiency of wind integration when compared with the conventional alternatives. The cost-benefit analysis has shown the economic justification and advantage of storage application for both social cost and transmission congestion relief over the gas-fired alternatives.