Daily operation of multi‐energy systems based on stochastic optimization considering prediction of renewable energy generation

Abstract

Nowadays, the energy crisis is one of the most critical challenges facing countries. To deal with this crisis, instead of independently optimizing each of the energy carriers (electricity, gas, heat etc.), all carriers are considered simultaneously by a concept called multi‐energy system, which not only leads to economic benefits but also has environmental benefits. Here, a mixed‐integer linear programming model is proposed to minimize the total daily cost of a local multi‐energy system including the cost of energy exchange with the main grid, the cost of natural gas, and carbon emission costs. A polynomial neural network model is used to forecast the hourly wind speed and radiation of the next day. Also, a probabilistic scenario‐generation and scenario reduction method is utilized to generate the possible scenarios from the probability density function. The simulation results show that the proposed model using neural network prediction and stochastic optimization increases the total cost of the multi‐energy system by 12% in the worst‐case. Sensitivity analysis of loads, electricity prices, and gas prices have been used to investigate the behaviour of variables on energy hub operating costs.

A two‐stage adaptive‐robust optimization model for active distribution network with high penetration wind power generation

“…(1) The first one is the 69-bus radial distribution network [36] as shown in Figure 6a, which consists of 68 branches, 15 DERs, 3 battery units and 3 wind farms. The detailed network branch parameters and the hourly load demand are cited from Table A1 of the appendix in [36], and the fuel cost coefficients of 15 DERs in the bus set {1, 5,9,16,20,25,28,35,37,44,54,56,58,59, 69} are cited from the Table A2. Three battery units are connected at buses 41, 50 and 65 with the rated energy capacity of 300 kWh and charging/discharging power limit of 150 kW.…”

“…In [8], a Sobol-augmented Latin hypercube sampling approach was presented to generate scenarios of uncertain wind power, and a stochastic day-ahead scheduling model was established to balance power system risk. In [9], a scenario generation-reduction method was utilized to generate representative scenarios for probabilistic renewables, and an SO model was presented to minimize the daily total cost of a multi-energy system. In [10], a multivariate Cauchy distribution was introduced for the forecasted wind power generation, and a chance-constrained real-time dispatch model was proposed to ensure system security and minimize system operational costs.…”

Decentralized Multi-Objective Energy Management With Dynamic Power Electronic Converters and Demand Response Constraints

“…To achieve this integration and interaction, the concept of multi-carrier energy systems and energy hubs was proposed. In [24], a hybrid neural network model and stochastic linear programming model are proposed to predict the power output of the RESs and minimize the daily operation cost and environmental impacts of the multi-energy systems that satisfy electrical, heating, and cooling loads. To enable additional flexibility in the energy provision, several hubs can cooperate to improve the system's efficiency.…”

“…The power generation of photovoltaic panels ( , ts PV P ) rely on variations in solar radiation ( , ts sor ), ambient temperature ( t AT ), time (t) in hours, and specific scenario (s). The comprehensive treatment of these factors, along with other relevant attributes, is thoroughly covered in reference [24].…”

Multi‐objective optimal planning of a residential energy hub based on multi‐objective particle swarm optimization algorithm

“…Numerous studies have been done in the realm of optimal operation of energy hubs [13][14][15]. For instance, the authors in [16] proposed a daily operation scheduling of an integrated energy system based on the concept of energy hub with the main aim of reducing costs along with emissions. Similarly, the authors in [17] developed a framework for obtaining optimal operation scheduling of an integrated local energy system with the presence of oxygen-enriched power plants and power-to-gas units.…”