Capacity optimization

Abstract: In an isolated power grid or a micro-grid with a small carbon footprint, the penetration of renewable energy is usually high. In such power grids, energy storage is important to guarantee an uninterrupted and stable power supply for end users. Different types of energy storage have different characteristics, including their round-trip efficiency, power and energy rating, self-discharge, and investment and maintenance costs. In addition, the load characteristics and availability of different types of renewable energy sources vary in different geographic regions and at different times of year. Therefore joint capacity optimization for multiple types of energy storage and generation is important when designing this type of power systems. In this paper, we formulate a cost minimization problem for storage and generation planning, considering both the initial investment cost and operational/maintenance cost, and propose a distributed optimization framework to overcome the difficulty brought about by the large size of the optimization problem. The results will help in making decisions on energy storage and generation capacity planning in future decentralized power grids with high renewable penetrations.

Abstract: In capacity optimization of hybrid energy storage station (HESS) in wind/solar generation system, how to make full use of wind and solar energy by effectively reducing the investment and operation costs based on the load demand through allocating suitable capacity of HESS is an optimization problem. The optimization objective is to minimize one-time investment and operation costs in the whole life cycle, the constraints are utilization rate, and reliability of power supply. In this paper, mathematical models of wind/solar generation systems, battery, and supercapacitor are built, the objective optimization function of HESS is proposed, and various constraints are considered. To solve the optimization problem, improved simulated annealing particle swarm optimization algorithm is proposed by introducing the simulated annealing idea into particle swarm algorithm. The new algorithm enhance the ability to escape from local optimum and improve the diversity of particle swarm, then help to avoid prematurity and enhance the global searching ability of the algorithm. With the example system, the optimization results show that the convergence of new algorithm is faster than the traditional particle swarm optimization algorithm and its cost optimization is better, which demonstrated the correctness and validity of the proposed models and algorithms. This method can provide a reference for the capacity optimization of HESS in wind/solar generation system.

Abstract: This paper presents a methodology for the joint capacity optimization of renewable energy (RE) sources, i.e., wind and solar, and the state-of-the-art hybrid energy storage system (HESS) comprised of battery energy storage (BES) and supercapacitor (SC) storage technology, employed in a grid-connected microgrid (MG). The problem involves multiple fields, i.e., RE, battery technology, SC technology, and control theory, and requires an efficient and precise co-ordination between sub-fields to harness the full benefits, making the problem labyrinthine. The optimization problem is formulated, and it involves a variety of realistic constraints from both hybrid generation and storage, and an objective function is proposed to: 1) minimize the cost; 2) improve the reliability; and 3) curtail greenhouse gases (GHG) emissions. The complex optimization problem is solved innovatively in piecewise fashion to decrease the complexity and computational time. First, sizes of solar photovoltaic (PV) and wind turbine (WT) are determined using an innovative search algorithm, and in the second step, the size of HESS is calculated, finally the optimal solution is determined. A comparison based upon cost, reliability, and GHG emissions is presented which plainly shows the effectiveness of the proposed methodology. The technique is also applied to determine the size of an MG employing PV, WT, and BES operating in grid-connected mode. And a brief cost analysis, reliability assessment, and emission reduction are given for three scenarios: 1) MG with HESS; 2) MG with BES; and 3) MG with conventional generation. It is shown that an MG with HESS is not only economical but also more reliable and has lower GHG emissions.