Optimal scheduling of multi-energy type virtual energy storage system in reconfigurable distribution networks for congestion management

The virtual energy storage system (VESS) is one of the emerging novel concepts among current energy storage systems (ESSs) due to the high effectiveness and reliability. In fact, VESS could store surplus energy and inject the energy during the shortages, at high power with larger capacities, compared to the conventional ESSs in smart grids. This study investigates the optimal operation of a multi-carrier VESS, including batteries, thermal energy storage (TES) systems, power to hydrogen (P2H) and hydrogen to power (H2P) technologies in hydrogen storage systems (HSS), and electric vehicles (EVs) in dynamic ESS. Further, demand response program (DRP) for electrical and thermal loads has been considered as a tool of VESS due to the similar behavior of physical ESS. In the market, three participants have considered such as electrical, thermal and hydrogen markets. In addition, the price uncertainties were calculated by means of scenarios as in stochastic programming, while the optimization process and the operational constraints were considered to calculate the operational costs in different ESSs. However, congestion in the power systems is often occurred due to the extreme load increments. Hence, this study proposes a bi-level formulation system, where independent system operators (ISO) manage the congestion in the upper level, while VESS operators deal with the financial goals in the lower level. Moreover, four case studies have considered to observe the effectiveness of each storage system and the simulation was modeled in the IEEE 33-bus system with CPLEX in GAMS

Optimal energy management and sizing of renewable energy and battery systems in residential sectors via a stochastic MILP model

Energy supply through integrated renewable energy sources (RESs) and battery systems will be of higher importance for future residential sectors. Optimal energy management and sizing for the components of residential systems can enhance efficiency, self-suffiency, and meanwhile can be cost-effective by reducing investment as well as operating costs. Accordingly, this paper proposes an exhaustive optimization model for determining the capacity of RESs, namely: wind turbines and photovoltaic (PV) systems. In this study, batteries and electric vehicles (EVs) are utilized in line with other sources to capture fluctuations of RESs. To model the uncertainties of RESs, energy prices, and load demands a linearized stochastic programming framework is applied. The proposed framework involves long-term and efficient resource development alongside with short-term management and utilization of these resources for supplying the demand load. In our study, we utilize the roulette wheel mechanism (RWM) method as well as proper probability distribution functions (PDFs) to generate scenarios for all sources of uncertainties, including wind turbines, PV systems, demand, and electricity market price. The approach is verified in two different cases, including an individual home and a larger micro-grid (MG). The results of multiple numerical simulations demonstrate the effectiveness of the proposed stochastic model

A multi-agent framework for electric vehicles charging power forecast and smart planning of urban parking lots

Abstract This paper proposes a novel stochastic agent-based framework to predict the day-ahead charging demand of electric vehicles (EVs) considering key factors including the initial and final state of charge (SOC), the type of the day, traffic conditions, and weather conditions. The accurate forecast of EVs charging demand enables the proposed model to optimally determine the location of common prime urban parking lots (PLs) including residential, offices, food centers, shopping malls, and public parks. By incorporating both macro-level and micro-level parameters, the agents used in this framework provide significant benefits to all stakeholders, including EV owners, PL operators, PL aggregators, and distribution network operators. Further, the path tracing algorithm is employed to find the nearest PL for the EVs and the probabilistic method is applied to evaluate the uncertainties of driving patterns of EV drivers and the weather conditions. The simulation has been carried out in an agent-based modeling software called NETLOGO with the traffic and weather data of the city of Newcastle Upon Tyne, while the IEEE 33 bus system is mapped on the traffic map of the city. The findings reveal that the total charging demand of EVs is significantly higher on a sunny weekday than on a rainy weekday during peak hours, with an increase of over 150kW. Furthermore, on weekdays higher load demand could be seen during the night time as opposed to weekends where the load demand usually increases during the day time