An analytical optimum method for simultaneous integration of PV, wind turbine and BESS to maximize technical benefits

Abstract

The present paper introduces an analytical approach for integrating distributed energy resources (DERs) and battery energy storage systems (BESSs) into power grids. The given method aims to simultaneously identify optimal buses and capacities of DERs and BESSs, with regard to the responsive load demand along with the stochastic nature of wind turbine (WT) and photovoltaic (PV) units, and then to minimize energy losses and improve voltage profiles. For this purpose, the responsive load model, the uncertainties in the WT and PV units, and the BESSs are firstly modeled. Secondly, the output curve of BESSs is obtained so that demand and supply can be balanced effectively, so as to utilize the full‐potential output of DERs. Thirdly, a set of formulation is developed for simultaneous integration of the DERs and the BESSs. This formulation reflects on different levels of loads and DERs for each period as well as each segment with their corresponding probability density. Finally, the proposed method is tested on the standard IEEE 33‐bus network. These results verify that sizing and sitting of DERs and BESSs can significantly shape the planning results of DERs and increase their penetration in the power grids at the same time.

Joint distribution of lightning and wind speed and its impact on the transmission system

“…Studies have shown that wind speed can generally be characterized by Weibull distribution (Khoubseresht et al, 2023), and the Weibull distribution function is given by…”

“…When high winds are accompanied with strong thunderstorms, the risk of the windage yaw flashover under lightning overvoltage increases significantly, and the joint probability distribution of lightning and wind speed should be taken into account in the wind deviation design of the transmission tower so as to ensure the protection effect under the combined meteorological conditions of thunderstorms and high winds. Under the influence of relevance, the statistical characteristics of the joint probability distribution of lightning and wind speed may be significantly different from the univariate distribution (Khoubseresht et al, 2023). Sklar proposed the copula theory, which can decompose the multidimensional joint distribution function into multiple marginal distribution functions and one copula function, and the copula function can describe the relevance characteristics among variables (Henderson et al, 2021).…”

Optimal Operation of Microgrids With Worst-Case Renewable Energy Outage: A Mixed-Integer Bi-Level Model

“…In [31], an optimal energy scheduling approach for a microgrid with electric vehicles, based on electricity market prices, is proposed to effectively manage the energy flow and charge/discharge of electric vehicles in the microgrid. In [32], an analytical optimization method is proposed for the integration of photovoltaic sources, wind turbines, and energy storage systems to improve microgrid voltage and losses. In [33], the authors have proposed a reinforcement learning (RL) approach for battery scheduling, to optimize user objectives in microgrid energy management.…”

Optimal coordinated energy management in active distribution networks considering battery energy storage and price‐responsive demand

“…This is because such coordinated management can absorb intermittency of renewable power generation. Otherwise, the intermittent nature of such energy sources can jeopardize the reliable operation of distribution networks [6]. Also, significant arbitrage benefits can be achieved under dynamic electricity pricing owing to BESS flexibility of dispatching energy at scheduled time.…”

“…Therefore, arbitrage benefits and desired operating strategy of dispatchable sources are the key issues that must be taken care of while solving the decision problem for profit maximization. Nevertheless, the problem is challenging on account of dynamic behaviour of the generation from RESs, demand, and electricity market that lead to a complex process which needs adaptive strategies to deal with BESSs [6]. The consideration of dynamic electricity price may further add computational burden to the optimization technique on account of dynamically varying system states with large number of state variables [7].…”