Optimal planning of multi‐energy microgrid with different energy storages and demand responsive loads utilizing a technical‐economic‐environmental programming

Abstract

This article proposes a planning model for a multi-energy microgrid (MEM) that supplies the electricity, heating, and cooling loads. This controls flexible demands and provides continuous control in the presence of smart and comprehensive programming of electricity, heat, ice, compressed air, and hydrogen energy storage. The features of the MEM are considering the losses and amortization costs of electricity, heating and cooling energy storage, and the operating area of the combined heat and power (CHP) units in the planning procedure. Besides, the principle of convexity in the CHP unit operation area is attended. The proposed formulation is applicable in days of summer and winter seasons for a variety of planning studies. The outcomes represent that utilizing the proposed MEM planning results in significant advantages for the power network and the consumer. The cost savings are respectively obtained equal to 33% and 34% in the winter and summer seasons. The cost savings can be considered besides the investiture cost of MEM elements to specifying the MEM profits made during the designing step. Another valuable result is the cost-saving of PHEV smart charge about 1.38 times of PHEV smart discharge for summer and about 1.05 for winter. It also makes the curve of demand optimal utilization from the demand response program and energy storage plan. K E Y W O R D S combined heat and power (CHP), demand response, energy planning, energy storage, multienergy microgrid (MEM), plug-in hybrid electric vehicle (PHEV)

An Optimal Energy Hub Management Integrated EVs and RES Based on Three-Stage Model Considering Various Uncertainties

“…The generated heat through the CHP unit is frequently proportional to its electric generation. In addition, heat operation constraints will be guaranteed if the electrical performance constraint is confirmed [32].…”

“…Case 1: In this case, the simple structure of the EH is assumed and both objective functions are minimized using the multi-stage solution method. At first, to investigate the difference between novel multi-stage method and other method presented in [32], case one is run with both methods and the result are shown in Table 7: Charge and discharge power of total EVs, ESS, and TSS It is clear from Fig. 18 that part of the rechargeable energy of electric vehicles is used to supply the demanded energy.…”

Techno‐economic‐environmental modeling, joint optimization, and sensitivity analysis of a combined water desalination‐hybrid renewable supply system

“…The power converter is a bidirectional device that can work in the inverter (DC to AC) or rectifier (AC to DC) mode. The converter can choose only one of these actions during any operation period 25 . Similar to the battery modeling, two binary variables and a sufficiently large constant are used in () to model this limitation. The power passing through the converter cannot exceed the nominal value.…”

Data-Driven Load Modeling to Analyze the Frequency of System Including Demand Response: A Colombian Study Case

“…Still, it is necessary to define the optimal demand shedding to improve the demand side participants' economic benefits with optimization models that consider the stochastic nature of demand. DR strategies adapted to the distribution system support adjusting the performance of DER into a smart grid and adapt the electricity market to a new agent and different form to interact with the users, [2], [15], [16].…”