Optimal Planning Design of a District-Level Integrated Energy System Considering the Impacts of Multi-Dimensional Uncertainties: A Multi-Objective Interval Optimization Method

Abstract

Improving the utilization efficiency of renewable energy sources (RES) is an important task for the development of an integrated energy system (IES). To address this challenge, this paper proposes a novel multi-objective interval optimization framework for the energy hub (EH) planning problem from the perspective of the source load synergy, while considering the impacts of both supply-and demand-side uncertainties. For this aim, based on an in-depth analysis of the adjustable characteristics of various loads in EH and their effect on RES absorption, an interval model is first established to describe the responsiveness of users' load demand to real-time energy price variations and its associated uncertainties. In view of the natural contradiction between the system's economic and environmental benefits, a multi-objective interval optimization model for the EH planning problem is developed, wherein the minimization of the system's economic costs and the maximization of the RES utilization rate are considered as the dual objectives to be optimized simultaneously. Moreover, this study takes into account the uncertainties of RES availability and demand-side behaviors by using interval numbers and properly considering their impacts in the context of long-term planning. According to the features of the proposed model, the interval order relation and possible degree method are jointly used to transform the model into a deterministic optimization problem first, and then an improved non-dominant sorting genetic algorithm is used to derive the optimal solution to the problem. The results show that the proposed method can effectively improve the economy of EH and the utilization efficiency of RES and flexibly meet different planning requirements, giving better engineering practicability.INDEX TERMS demand-side management, improved non-dominated sorting genetic algorithm, multiobjective interval optimization model, source load synergy, uncertainty analysis

Review of Uncertainty Modeling for Optimal Operation of Integrated Energy System

“…Considering demand response and wind power uncertainty, a coordination operation strategy based on interval optimized integrated energy systems is proposed (Su et al, 2017). Liu et al (2021) From the perspective of source load synergies, consider the uncertainty of supply and demand, proposing a new multi-target interval optimization framework for energy hub planning issues.…”

“…Secondly, with the transition to a fully renewable energy system, it is more challenging to optimize operation under uncertainty in terms of uncertainty modeling. Through systematic investigations, we have identified several major uncertainty modeling methods that have been applied, including probabilistic methods (Fang et al, 2020), robust optimization (Wang et al, 2019a), interval analysis/probability methods (Shahidinejad et al, 2012;Liu et al, 2021), information gap decision-making theory (Zhao et al, 2017a), and hybrid optimization methods (Guo et al, 2019). Thirdly, there are many IES coupling devices, and the characteristics of the devices, complex network constraints, and multiple uncertain factors lead to highly nonlinear terms in the model.…”

Resilience enhancement strategy of multi-energy coupling distribution network considering movable energy storage equipment

“…Zhou et al [7] use a nonparametric kernel density estimation method to calculate the probability distribution of resilience, which can fully reflect the contribution of power generation with random fluctuation distribution to the resilience of the distribution network. Reference [8] takes the elastic resilience of the power system as the optimization objective and obtains the power grid dispatching scheme through the optimization algorithm. Liu et al [9] focus on the grid elasticity of electricity-gas grid coupling and model the recovery process of the power grid and gas grid after different types of natural disasters based on the Markov state transition process to verify that multi-energy coupling can improve the elasticity of the power system.…”

Energy storage capacity planning method for urban integrated energy system considering uncertainty

“…The coordinated development of renewable energy and conventional energy has become the development path of my country's energy production. The core of the integrated energy system is electricity, which simultaneously integrates various energy forms such as electric energy, wind energy, photovoltaic energy and biomass energy, covering multiple energy supply links such as power generation, transmission, distribution, electricity consumption and energy storage [4] . However, most renewable energy belongs to distributed energy and should be utilized in the form of distributed system.…”