An Optimal Energy Management Method for the Multi-Energy System with Various Multi-Energy Applications

Abstract

As the development of the multi-energy system (MES), various ME applications are deployed. ME applications not only bring advanced functionalities to the MES, but also show great potentials in promoting the operation performance of the MES, especially improving the accommodation of renewable energy sources (RES). However, the realization of these potentials largely relies on the energy management, which shall facilitate the effective function of each ME application and the coordinated collaboration of all the ME applications. Without a comprehensive energy management methodology, ME applications may mutually interfere, which not only hinder the RES utilization, but also may harm the MES operation performance. In this premise, this paper integrates the energy management model of the combined cooling, heat and power plants, power-to-hydrogen/gas-to-power plants, and demand side management model of the EV charging loads into the energy management model of the MES, and proposes an comprehensive optimal day-ahead energy management framework to simultaneously improve the profit, RES utilization rate, and energy saving performance of the MES. To address the proposed optimization model, Elitist Non-dominated Sorting Genetic algorithm II algorithm is employed to heuristically find the Pareto-optimal results. Finally, case studies prove the effectiveness of the proposed methodology.

Optimal Tuning of Model Predictive Controller Weights Using Genetic Algorithm with Interactive Decision Tree for Industrial Cement Kiln Process

“…Unlike the proposed SoGA, MoGA does not combine individual metrics. It provides a Pareto front [33] of the MPC weights, which represents the boundary traced between the feasible and infeasible solution space. It describes the dependencies of the metrics (tracking error and energy utilization) and also provides an insight on possible minimization of them.…”

“…Linear models such as transfer function, state-space, ARX (autoregressive-exogenous-input), and ARMAX (autoregressive-moving-average with exogenous inputs) are investigated. The model order and the number of delay samples are chosen using Hankel singular value, which describes the energy of the state variables [33]. The number of state variables having higher energy is determined as the order of the model.…”

Thermodynamic Performance Analyses and Optimization of Dual-Loop Organic Rankine Cycles for Internal Combustion Engine Waste Heat Recovery

“…The DORC parameters are listed in Table 6 [43]. The genetic algorithm has been extensively used for optimization [6,37,[45][46][47][48][49][50][51]. Yang et al [6] selected a net power output per unit heat transfer area and exergy destruction rate as the objective functions and optimized evaporation pressure, superheat degree, and condensation temperature for ORC using the genetic algorithm.…”

Cloud-Fog Architecture Based Energy Management and Decision-Making for Next-Generation Distribution Network with Prosumers and Internet of Things Devices

“…In [19], an optimal power dispatch on a 24h basis for distribution systems, including large-scale controllable loads, is presented with a swarm particles optimization algorithm. The genetic algorithm (GA), as a heuristic algorithm, does not depend on the specific domain of the problem and is robust enough to be used in complex and difficult optimization problems in power systems [20][21][22]. Most of the previous work have focused on formulating the optimization problem to achieve economic efficiency in a conventional centralized way, without considering how the architecture of this energy management might improve operational preformation with emerging information and communications technology.…”