Multi-Objective Optimization Dispatching Strategy for Wind-Thermal-Storage Generation System Incorporating Temporal and Spatial Distribution Control of Air Pollutant Dispersion

Abstract

Temporal and spatial distribution (TSD) model presented in our previous work of air pollutants is an effective model in describing the increment ground level concentration caused by power generation. In this paper, the newly emerging temporal and spatial characteristics of power dispatch when incorporating the TSD model are studied. Firstly, a multi-objective optimization dispatching model for wind-thermalstorage generation system is proposed. In the time dimension, the model can coordinate multiple generation sources in the face of atmospheric condition variation. In the space dimension, the correlations between power plants location, pollutant diffusion paths and atmospheric boundary layers are considered. Secondly, chance constraints are adopted to address stochastic variables, while the stochastic formulation is transformed into a deterministic one based on wind power distribution. Then a multi-objective optimization method is employed to obtain a desired Pareto front. Case studies are carried out on modified IEEE 39-bus system and Guangdong grid system under four strategies, which validate the performance of the proposed model and the effectiveness of the strategy. INDEX TERMS Environmental economic dispatch, comprehensive pollution evaluation value, geographic grid, temporal and spatial characteristics, multi-objective optimization. NOMENCLATURE INDICES WEICONG WU received the B.S. degree in electrical engineering from Huaqiao University, Xiamen, China, in 2018. He is currently pursuing the M.S. degree in electrical engineering with the School of Electric Power Engineering. His research interest includes optimal operation of power systems.

Adaptive Sampling Immune Algorithm for Multi-Objective Probabilistic Optimization

“…For example, Wu et al 38 constructed a two-level multi-objective chance-constrained programming model for source-load coordination in an integrated energy system, treated the stochastic objective function using Monte Carlo simulations and solved the upper objective function using a game that achieves a Nash equilibrium while solving the lower objective function using a firefly algorithm. Li et al 5 established a multi-objective chance-constrained programming model for joint wind and fire dispatching with consideration of economic and environmental benefits. They used Monte Carlo stochastic simulation to estimate the objective function values and obtained the Pareto-optimal solution set of this model by the normal boundary crossing method and the primal-dual interior point method.…”

“…Monte Carlo stochastic simulation has been widely used in stochastic programming because of its simplicity, convenience, and suitability for noise of any distribution type. In terms of handling noise in the objective function by Monte Carlo stochastic simulation, the sampling types are broadly divided into three categories: static sampling 2,[4][5][6][7][8][31][32][33][34][35][36][37][38] , dynamic sampling 39 , and adaptive sampling 17,20,21,40 . Static sampling is a simple and easy-to-use sampling method and requires that all individuals are assigned the same and sufficiently large sample size to obtain an estimate that approximates the true value, which inevitably leads to higher computational complexity.…”

“…Multi-Objective Probabilistic Optimization Problem (MOPOP) is a class of stochastic programming problems with extensive engineering application background 1 , where there are multiple conflicting objective functions to be optimized that satisfy probabilistic constraints. MOPOPs have been applied in many practical engineering areas, such as water resource operations 2,3 , airborne ground operations 4 , coordinated power grid and electricity dispatch 5,6 , electric vehicle charging stations 7 , service resource scheduling 8 , distributed energy 9 , bi-level optimal dispatching 10 , advertising promotion 11 , and so on. These engineering problems usually contain random parameters or random variables (referred to as noise), and are characterized by complex model structure, unknown noise distribution information, multiple conflicting objective functions, non-linearity, and multimodality, which make MOPOPs very challenging to solve.…”

Environmental Economic Dispatch Strategy for Power-Gas Interconnection System Considering Spatiotemporal Diffusion of Air Pollutant and P2G in Coastal Areas

“…The current research on environmental economic dispatch mainly optimize dispatch schedules from the perspective of reducing pollutant emissions, including setting emission quotas for power generation pollutant emissions and setting fines for excess [10]- [12], setting pollutant discharge constraints [13], [14],or constructing an environmental economic multi-objective optimization model which take pollutant emissions as one of the objectives [15]. However, optimization from the perspective of reducing the amount of pollutant emissions reduces the total amount of pollutant emissions one-sidedly, but the ground pollutant concentration may not decrease [16].…”