The Integration of Wind-Solar-Hydropower Generation in Enabling Economic Robust Dispatch

Abstract

Currently the renewable energies including wind power and photovoltaic power have been increasingly deployed in power system to achieve contamination free and environmental-friendly power production. However, due to the natural characteristics of wind and solar, both wind power and photovoltaic power contain uncertainty and randomness which may significantly impact the stability, security, and economic efficiency of the conventional power system mainly consisted by hydropower and thermal power. To deal with the issue, this paper presents a two-stage robust model which is able to achieve the optimal day-ahead dispatch strategy in the worst-case scenario of wind and photovoltaic outputs. Because of the strong interactions between the two stages, the original optimization has been decomposed into the day-ahead dispatch master problem and the additional adjustment subproblem considering the uncertainty and randomness of the wind and the photovoltaic outputs. Also, the piecewise linearization technique is employed to convert the original problem into a MILP problem. Afterward, the dualization of the additional adjustment subproblem can be obtained by using linear programming strong duality theory. Additionally, the Big-M method enables the linearization of the dual model. The interacted-iterations between the master problem and the subproblem are successfully implemented which can ultimately figure out the optimal day-ahead dispatch strategy of the power system with conventional and renewable energies.

Hydro–Connected Floating PV Renewable Energy System and Onshore Wind Potential in Zambia

“…Such scholarly analyses, however, introduce errors in the modeling process by omitting to include the stochastic tendencies of solar PV and wind power [61][62][63]. Furthermore, by using deterministic and stochastic programming, Wei and Liu [64] tackled the uncertainties of solar PV and wind systems. The enhanced system security, coupled with limitations on the system economy and flexibility resulting from the deterministic inclusion of spinning reserve to the dispatch model, were revealed by Wei and Liu [64] and Liu et al [53].…”

“…Furthermore, by using deterministic and stochastic programming, Wei and Liu [64] tackled the uncertainties of solar PV and wind systems. The enhanced system security, coupled with limitations on the system economy and flexibility resulting from the deterministic inclusion of spinning reserve to the dispatch model, were revealed by Wei and Liu [64] and Liu et al [53]. Dong et al [65] and Zou [66] revealed that by adopting a structured multi-scenario perspective, a stochastic optimization problem in nature was able to be converted to a deterministic one, with the inaccuracy in the optimization output being the main trade-off.…”

“…Researchers [69] had developed a method to track real-time deviations between two consecutive energy-scheduling intervals, while attaching the variability and uncertainty cost of energy. Another research study [64] incorporated the energy curtailment of solar PV and wind as a penalty cost in the scheduling. The decision-making process regarding the suitability and location of sites for variable renewable energy sources (solar PV and wind) utilizes geospatial parameters, mainly involving GIS models in dynamic analysis (i.e., to capture, analyze, store, manage, and manipulate spatial or geographical data) [70][71][72][73].…”

Adjustable robust optimization in enabling optimal day-ahead economic dispatch of CCHP-MG considering uncertainties of wind-solar power and electric vehicle

“…Consequently, because of the uncertainties modeling ability using a large-size of stochastic scenarios, SO has been widely employed to solve the power system optimization tasks [30,34,24,16,29,44]. Although the authors claimed that SO can significantly reduce the expected operational cost than DO-based methods do, research [35] pointed out that SO-based decisions are overoptimistic as the worst-case scenarios are almost ignored. Additionally, two flaws of SO still need to be further addressed: massive scenarios result in computationally intensive issue [43,17]; it is difficult to obtain precise probability distribution function (PDF) of the uncertainties in practice [18].…”

“…And then, to facilitate the solution, the model is decomposed into a mixed integer linear programming (MILP) master problem (MP) and sub problem (SP) using column-and-constraint generation (C&CG) method [45]. Following, SP is derived into MILP using Big-M method [35] and duality theory [17,19]. At last, C&CG iteration algorithm is adopted to solve MP and SP, which finally provides the optimal DED strategy.…”