Optimal Planning of Energy Storage Systems in Power Transmission Networks Considering Wind Farms

This thesis is composed of two sections. In the first section, a probabilistic discretising method is derived and developed to discretise the continuous joint power distribution of correlated wind farms. Combining the probabilistic discretizing method with a multi-objective hybrid particle swarm optimisation (MOPSO) and non-dominated sorting genetic algorithm (NSGAII), a new hybrid probabilistic optimisation algorithm is proposed. The proposed hybrid algorithm aims to search for the best location and size of energy storage system (ESSs) and considers the power uncertainties of multi correlated wind farms. The objective functions to be minimised include a system’s total expected cost restricted by investment budget, total expected voltage deviation and total expected carbon emission. IEEE 30-bus and IEEE 57-bus systems are adopted to perform the case studies using the proposed hybrid probabilistic optimisation algorithm. The simulation results demonstrate the effectiveness of the proposed hybrid method in solving the optimal allocation problem of ESSs and considering the uncertainties of wind farms’ output power and the correlation among them. The expected cost, emission, voltage deviation and power losses reduced by 66.99%, 60.54%, 71.13% and 33.03%, respectively, in the case study of IEEE 30-bus system and decreased by 36.12%, 88.90%, 81.74% and 68.49%, respectively, in the case study of IEEE 57-bus system, compared to the cases without considering ESSs. In the second section, bi-level mixed integer non-linear optimisation planning and operation model is formulated for the optimal configuration (location, capacity and power ratings) of compressed air energy storage system (CAES) in power transmission networks. The model was formulated with consideration for independent and iv correlated wind farms. The single objective function in the inner layer of the bi-level model includes the difference between the total daily expected operational cost of conventional generators and the energy arbitrage benefits derived when considering the operational strategies of ESSs. The outer layer is a multi-objective function composed of three objective functions to be minimised. The objective functions encompass the total daily expected planning and operational cost, total daily expected emission and the maximum expected voltage deviation. Wind power uncertainties in independent and correlated wind farms were also examined. A hybrid non-dominating sorted genetic algorithm and multi-objective particle swarm optimisation were used to minimise the outer layer objective function, whilst fast tabu search algorithm that considers the probabilistic load flow represented by wind power uncertainties and the operational strategies of ESSs was adopted to minimise the inner layer objective function. An IEEE 57-bus system was subjected to a case study using the proposed two-stage model. The simulation results confirmed the advantage of considering the benefits of a peak shaving operational strategy from economic, technical and environmental points of view. The total daily expected cost, emission and maximum voltage deviation were reduced by 1.077%, 14.756% and 21.055%, respectively, after considering the peak shaving operation strategy considering independent wind farms Keywords: Energy Storage System (ESS); Correlated Wind Farms; Probabilistic Load Flow (PLF); Operational Strategy; Two-Stage Optimization Problem; Non-Dominated Sorting Genetic Algorithm (NSGAII); Multi-Objective Particle Swarm Optimization (MOPSO); Tabu Search Algorithm (TSA).