A New Hybrid-Adaptive Differential Evolution for a Smart Grid Application Under Uncertainty

Abstract

Power systems are showing a dynamic evolution in the last few years, caused in part by the adoption of smart grid technologies. The integration of new elements that represent a source of uncertainty, such as renewables generation, electric vehicles, variable loads and electricity markets, poses a higher degree of complexity causing that traditional mathematical formulations struggle in finding efficient solutions to problems in the smart grid context. In some situations, where traditional approaches fail, computational intelligence has demonstrated being a very powerful tool for solving optimization problems. In this paper, we analyze the application of Differential Evolution (DE) to address an energy resource management problem under uncertain environments. We perform a systematic parameter tuning to determine the best set of parameters of four stateof-the-art DE strategies. Having knowledge of the sensitivity of DE to the parameter selection, self-adaptive parameter control DE algorithms are also implemented, showing that competitive results can be achieved without the application of parameter tuning methodologies. Finally, a new hybrid-adaptive DE algorithm, HyDE, which uses a new "DE/target − to − perturbed best/1" strategy and an adaptive control parameter mechanism, is proposed to solve the problem. Results show that DE strategies with fixed parameters, despite very sensitive to the setting, can find better solutions than some adaptive DE versions. Overall, our HyDE algorithm excelled all the other tested algorithms, proving its effectiveness solving a smart grid application under uncertainty.

Smart Microgrids Operation Considering a Variable Neighborhood Search: The Differential Evolutionary Particle Swarm Optimization Algorithm

“…Although these inputs are assumed to be highly accurate, there is high implicit uncertainty in the estimation process [6]. Therefore, optimization techniques used to solve this energy management problem must exhibit low sensitivity to these uncertainty parameters [17,18].…”

“…A large number of scenarios increases model accuracy, but it has high computation costs due to high variations in the parameters. For this reason, in this work a reduction technique was used to keep a small number of scenarios while maintaining the main statistical features of the initial scenarios [6,17].…”

“…Moreover, in [13] generation forecasts with renewable resources are defined in discrete times, in addition DER such as ESS are distributed in the microgrid [14] and minimize investment and operational costs [13,15]. EVs have charging and discharging capability [16,17] and are managed by an aggregator that uses them as network resources along with ESS [6,7,[18][19][20]. Finally, electricity price forecasts complement the operation of the microgrid [21].…”

“…The literature proposes that the inclusion of sources of uncertainty such elements include renewable energies, load forecasting, EVs and the price market; they economically and technically affect the operator decisions of the microgrid [11][12][13][14][15][16][17][18][21][22][23][24][25] (Table 1). Table 1 highlights one of the most complete works of Khodaei, 2015, Saber 2012 and Farzan 2015, which include high difficulty in the solution with uncertainty in renewable energy, demand forecast and electric vehicles.…”

“…Therefore, in Table 2, each solution is encoded as a vector consisting of six groups of variables that are repeated sequentially throughout the 24 periods (hours) of optimization. The following groups of variables are presented: (1) Active power of distributed generation (DGs) and external suppliers; (2) Binary generators; (3) and (4) Charge and discharge of EVs and ESS; (5) Demand response; and (6) Markets [5,17,18,26].…”