Number Of Energy Storage Systems Research Articles

Fast Solving Method for Two-Stage Multi-Period Robust Optimization of Active and Reactive Power Coordination in Active Distribution Networks

This paper considers constraints on the cycle number of energy storage systems (ESSs), travel distances of switchable capacitors reactors (SCRs), on load tap changers (OLTCs) and step voltage regulators (SVRs). The characteristics and operational constraints of these equipments are properly formulated with binaries and big-M method, obtaining desirable linear descriptions. The branch flow equations of distribution systems for active and reactive power coordination are constructed. Considering physical constraints, a multi-period mixed-integer deterministic second-order conic programming (SOCP) to minimize network losses is formulated. Then, considering uncertainties of loads and active power of renewable distributed generators (DGs), a two-stage robust model is developed. A directly iteratively solving method between the first and second stage models is proposed. In contrast to the traditional column-and-constraint generation (CCG) method, increases to variables and constraints are not needed to solve the first stage model. To solve the second stage multi-period model, only the model of each single period needs to be solved first. Then their objective function values are accumulated, and the worst scenarios of each period are concatenated. As a result, the solving complexity is greatly reduced. The capabilities of the proposed method are validated by three simulation cases. It is found that the computational rate using the proposed method is significantly enhanced with much less computer storage. Specifically, the simulation results of 4, 33 and 69-bus systems indicate that the computing rate of the proposed method is about 28 times higher than CCG method when 8 iterations are performed. Meanwhile, the precision of optimization results is also improved.

Identification of Critical Parameters for the Design of Energy Management Algorithms for Li-Ion Batteries Operating in PV Power Plants

Lithium-ion batteries are gaining importance for a variety of applications due to their price decrease and characteristics improvement. For a proper use of such storage systems, an energy management algorithm (EMA) is required. A number of EMAs, with various characteristics, have been published recently, given the diverse nature of battery problems. The EMA of deterministic battery problems is usually based on an optimization algorithm. The selection of such an algorithm depends on a few problem characteristics, which need to be identified and closely analyzed. The aim of this article is to identify the critical optimization problem parameters that determine the most suitable EMA for a Li-ion battery. With this purpose, the starting point is a detailed model of a Li-ion battery. Three EMAs based on the algorithms used to face deterministic problems, namely dynamic, linear, and quadratic programming, are designed to optimize the energy dispatch of such a battery. Using real irradiation and power price data, the results of these EMAs are compared for various case studies. Given that none of the EMAs achieves the best results for all analyzed cases, the problem parameters that determine the most suitable algorithm are identified to be four, i.e., desired computation intensity, characteristics of the battery aging model, battery energy and power capabilities, and the number of optimization variables, which are determined by the number of energy storage systems, the length of the optimization problem, and the desired time step.

Operation of pumped storage hydropower plants through optimization for power systems

Worldwide, there is an increase in the number of energy storage systems that are installed as a result of several benefits. These systems bring uniformity and efficiency improvements to electrical grids by storing and returning energy to the grid. They help with the integration of the new renewable energy sources, mitigating the intermittency of these sources, which is the main problem to implement them on a large scale. One of the most widespread kinds of these systems is the Pumped Storage Hydropower Plant, with an installed power capacity of 153 GW at global level. This work presents a new Mixed Integer Linear Programming model to operate these plants by maximizing the received profits. The model is distinguished from others because it allows the inclusion of a greater number of breakpoints, which means that more realistic solutions can be obtained by reducing the computational effort. To prove the usefulness of the formulation, two real plants located in Argentine Republic are tested: Rio Grande and Los Reyunos power plants which have a total installed power capacity of 975 MW. Results indicate that the proposed model reaches feasible solutions with a sufficient level of accuracy with CPU times of less than 1 s.

Optimal Allocation of Energy Storage Systems for Voltage Control in LV Distribution Networks

This paper addresses the problem of finding the optimal configuration (number, locations, and sizes) of energy storage systems (ESSs) in a radial low voltage distribution network with the aim of preventing over- and undervoltages. A heuristic strategy based on voltage sensitivity analysis is proposed to select the most effective locations in the network where to install a given number of ESSs, while circumventing the combinatorial nature of the problem. For fixed ESS locations, the multi-period optimal power flow framework is adopted to formulate the sizing problem, for whose solution convex relaxations based on semidefinite programming are exploited. Uncertainties in the storage sizing decision problem due to stochastic generation and demand, are accounted for carrying out the optimal sizing over different realizations of the demand and generation profiles, and then taking a worst-case approach to select the ESS sizes. The final choice of the most suitable ESS configuration is done by minimizing a total cost, which takes into account the number of storage devices, their total installed capacity and average network losses. The proposed algorithm is extensively tested on 200 randomly generated radial networks, and successfully applied to a real Italian low voltage network and a modified version of the IEEE 34-bus test feeder.

A Study of Wayside Energy Storage Systems (WESS) for Railway Electrification

Results of a 1982 study of wayside energy storage systems (WESS) for railway electrification are summarized. The study was performed by SNC Inc. for the Transportation Development Center of Transport Canada (TDC). WESS introduces savings in the overall costs of the electric energy supplied to the catenary, by reducing the peak load seen by the utility and, if locomotive regeneration is available, the total energy demand. A number of energy storage systems were evaluated for near-term WESS applications. Two were found to be practicable: lead-acid batteries and steel flywheels. The required battery technology already exists and installations, existing or planned, approach the required levels of stored energy and power. For steel flywheels existing systems have available stored energy levels of less than one thirtieth of that required for WESS. Detailed engineering studies would be necessary to demonstrate that single wheel systems of the capacity required could be manufactured at reasonable cost. Budget estimates were derived for the combined installation and operating costs of battery and flywheel WESS. These were compared with the potential savings in the costs of electricity supply for a 20-year operating life, using average Canadian Utility rates and typical WESS parameters.