Optimal Energy Management for Microgrids Considering Uncertainties in Renewable Energy Generation and Load Demand

Abstract

This paper proposes an efficient power management approach for the 24 h-ahead optimal maneuver of Mega–scale grid–connected microgrids containing a huge penetration of wind power, dispatchable distributed generation (diesel generator), energy storage system and local loads. The proposed energy management optimization objective aims to minimize the microgrid expenditure for fuel, operation and maintenance and main grid power import. It also aims to maximize the microgrid revenue by exporting energy to the upstream utility grid. The optimization model considers the uncertainties of the wind energy and power consumptions in the microgrids, and appropriate forecasting techniques are implemented to handle the uncertainties. The optimization model is formulated for a day-ahead optimization timeline with one-hour time steps, and it is solved using the ant colony optimization (ACO)-based metaheuristic approach. Actual data and parameters obtained from a practical microgrid platform in Atlanta, GA, USA are employed to formulate and validate the proposed energy management approach. Several simulations considering various operational scenarios are achieved to reveal the efficacy of the devised methodology. The obtained findings show the efficacy of the devised approach in various operational cases of the microgrids. To further confirm the efficacy of the devised approach, the achieved findings are compared to a pattern search (PS) optimization-based energy management approach and demonstrate outperformed performances with respect to solution optimality and computing time.

Using Stochastic Dual Dynamic Programming to Solve the Multi-Stage Energy Management Problem in Microgrids

“…It should also be noted that an alternative approach prevalent in the literature models price dynamics using a Markov chain, initially proposed in [50]. However, the assumption that prices evolve according to a homogeneous Markov chain remains a topic of debate [51], highlighting the complexity and variability in modeling financial variables within microgrid systems.…”

“…The backward recursion involves computing these sub-gradients 𝛽 , using the dual values of the linear subproblem of each stage 𝑡. For a more detailed explanation on Backward pass and the calculation of sub-gradients using dual values, please refer to [37,49,50].…”

A comparative study for two Novel Optimization Algorithms used to solve Microgrid Energy Management problem considering Energy Storage System

“…A wide variety of optimization techniques and algorithms have been utilized to solve different optimization challenging problems of MG systems. These tactics seek to optimize a number of MG characteristics, including energy management [5], cost [6], ESSs [7]. Authors in [8], optimally managed MG's energy including RESs, ESSs, and load management, by using Mixed-Integer Linear Programming (MILP) model.…”

Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations

“…HESS technology faces multiple optimization problems, i.e., sizing, capacity, and power distribution, since it is still an emerging technology. Thus, several optimization techniques such as genetic algorithms (GA) and ant colony optimization (ACO) were adopted in the literature to fix these issues (see Table 1) [14][15][16][17][18]. In [14], the authors propose an optimal charge-scheduling algorithm for EV based on day-ahead PV power forecasts in order to minimize the total charging costs.…”

“…In [15], an optimization model and energy management schemes for microgrids to increase the efficiency of EV are suggested. In [16], the authors propose a 24-h-ahead operational timeline at an hourly resolution model, integrating RE to minimize costs. In [17], the authors propose a combination of genetic algorithms (GA) and dynamic programming (DP) to create charge/discharge schedules for ESSs within the context of time-of-use pricing and RE integration.…”