This paper proposes an innovative energy management strategy for a microgrid (MG) integrated with electric vehicles (EVs), battery energy storage systems (BESS), and renewable energy sources. The aim is to optimize the MG operation by incorporating price-based demand response (PBDR) and incentive-based demand response (IBDR). The proposed demand response mechanism benefits MG operators and end users. The objective of the MG operator is to reduce the power purchase cost from the grid, minimize the fuel cost of conventional distributed generators, and maximize revenue generated by consumers participating in DR. The proposed objective encourages energy consumers to participate in the DR program by minimizing their electricity bills and offering incentives. The fuzzy max-min method is approached to obtain optimal solutions as both objectives are conflicting. This work addresses the uncertainties in renewable energy sources, load demand, and electricity prices using Hong’s (2m + 1) point estimation method. The proposed method leads to a 13.86% energy cost saving for MG operators.

This paper proposes a novel customer dissatisfaction-constrained price-based demand response (PBDR) program implemented to cope with the increasing energy needs. The load aggregator (LA) collects the customer’s priority setting and the preferred load-shift intervals. It then optimizes the energy consumption pattern to satisfy the end-user’s profit, taking his/her dissatisfaction cost into consideration. The microgrid operator (MGO) communicates with the load aggregator to collect the optimized load pattern and incorporates it into the microgrid’s energy management structure to boost the technical and economical attributes of microgrids. Numerous case studies show that the proposed algorithm is quite effective in decreasing the cost for the microgrid operator, as well as ensuring consistent user participation in the DR program with enhanced benefits.

An active distribution network must coordinate distributed co-generation plants (CGPs), renewable energy sources, energy storage systems (ESSs), power electronic devices, plug-in hybrid electric vehicles (PHEVs), reactive power compensator (RPC), auxiliary units, loads, power supply restoration and network reconfiguration to operate optimally. In this regard, this study presents a two-stage coordinated energy management strategy among several units with varying ownership of a zero bus microgrid (ZBMG). In the first stage of the proposed design, the effects of ESS units and PHEV operation in grid-to-vehicle and vehicle-to-grid modes are examined. The following stage of this framework considers CGP and auxiliary unit power distribution to meet an economic-emission objective. A stochastic Monte Carlo simulation (MCS) with Latin hypercube sampling (LHS) and Nataf transformation (NT) is used to represent the uncertainties of load, correlated renewable generation, and the charging/discharging power of ESSs and PHEVs. On the other hand, information gap decision theory is used to simulate the grid energy pricing uncertainty. A hybrid layout including probability and information gap is used to solve the optimization problem. Moreover, this stage deals with a power supply restoration issue in the event of a planned or unplanned branch opening. The efficacy of the framed problem is validated through simulation.

This paper presents an energy management strategy for multi networked microgrids (MGs) comprising of dispatchable and non-dispatchable distributed generation, energy storage system (ESS), and plug-in hybrid electric vehicles (PHEV). The proposed approach allows the cooperation for the direct energy exchange among the microgrids and also with the utility grid. This study investigates the impacts of stochastic charging effects of PHEVs and intermittencies of the renewables and the load demands on the proposed strategy. Furthermore, the active participation of the microgrids in the demand response (DR) program by transferring flexible loads in response to high pricing signals aid in reducing the overall cost of microgrids. The energy management (EM) problem has been formulated as a multi-objective optimization problem of minimization of the microgrids’ overall cost and emission and maximization of the distribution network operator’s (DNO) profit. Simulation results are illustrated to validate the effectiveness of the proposed method.

This paper presents the operation of islanded microgrid (IMG) with dispatchable DG units, battery and shunt capacitors considering load growth up to planning period. The dispatchable DG units in the IMG can be able to meet load demand up to the end of the planning period due to their inherent droop nature. The battery and shunt capacitors inject the fixed amount of active and reactive power into the system irrespective of load growth; however, they were improving the voltage and minimizing the power loss in the system. The averaged seasonal load profile pattern and Modified Newton Raphson (MNR) load flow method are integrated into the power flow analysis. This paper solves the operation of an IMG system in two cases; in which case(a) considers only biomass DG units and case(b) deals battery and shunt capacitors along with biomass DG units. A net profit based objective function is evaluated by taking various costs of components in both cases up to planning period. The effectiveness of the proposed method is demonstrated on a 33-bus distribution system operating in islanded mode.

A method of optimal siting and sizing of the battery-based energy storage system (BESS) is presented in this paper. The BESS is installed with an objective to minimize the operating cost of the microgrid. The BESS site is chosen based on a new index proposed called Locational Economic Driving Force, which is based on locational marginal price (LMP). AC optimal power flow (ACOPF) problem is formulated and solved for a radial autonomous microgrid. The proposed ACOPF can calculate the components of LMP, such as energy, loss, and congestion. The proposed technique is applied to the 33-node microgrid, and a considerable reduction in net operating cost was achieved.