Development of an integrated multi-objective optimization model for determining the optimal solar incentive design

Abstract

Summary To promote the deployment of the solar photovoltaic (PV) system from the long‐term perspective, the solar PV industry in many countries still needs the financial support from the government despite its remarkable growth and price reductions in the last decade. Many countries with this financial burden on their government budget, however, are planning to reduce or to expire the financial support step by step. To bring the solar PV market to its full maturity, it is crucial to improve the solar policies and to sustain the financial support with acceptable and reasonable prices, which can maximize the benefits for the investors while minimizing the incentive budget for the government. Towards this end, this study aimed to develop an integrated multi‐objective optimization (iMOO) model for determining the optimal solar incentive design from the perspectives of the investor and the government. A Microsoft Excel‐based iMOO model was developed using life cycle cost analysis, genetic algorithm, and Pareto optimal solutions. The developed Microsoft Excel‐based iMOO model was applied to six target regions to verify its effectiveness in determining the optimal solar incentive design. As a result, it was shown that depending on the various characteristics (e.g., solar radiation, electricity price, and installation cost) of a region, the optimal solar incentive design can be differently determined with a reasonable and acceptable level using the developed iMOO model. Among the six target regions, Newark required the lowest incentive budget of $US10,648.41 whereas Oklahoma City required the highest incentive budget of $US20,648.73 to offer their optimal solar incentives. The model developed in this study can help both the investor and the government in a decision‐making process and provide some solutions and insights for planning solar policies and strategies. Copyright © 2017 John Wiley & Sons, Ltd.

A novel optimization model for combined wind power accommodation and electric boiler with thermal storage

“…Furthermore, single‐objective optimization methods were usually employed to solve optimization problems, and the factors considered in the objective function were different. Lee et al developed an integrated multi‐objective optimization model considered both investor benefits and the government budget to obtain the optimal solar incentive design. To address this challenge of utilization of EBTS to improve wind power accommodation, multi‐objective is considered to be employed for optimization model in our study.…”

Evaluation of the photovoltaic potential in built environment using spatial data captured by unmanned aerial vehicles

“…1,2 Approximately 94.8 GW PV components were installed worldwide as of 2018, a 23% increase over 2017 reaching a cumulative capacity of 496.8 GW. 1,2 Approximately 94.8 GW PV components were installed worldwide as of 2018, a 23% increase over 2017 reaching a cumulative capacity of 496.8 GW.…”

“…Photovoltaic (PV) technologies are commonly utilized to reduce greenhouse gases as a part of many national development plans across the world. 1,2 Approximately 94.8 GW PV components were installed worldwide as of 2018, a 23% increase over 2017 reaching a cumulative capacity of 496.8 GW. 3 The existing literature on built environment applied photovoltaics (BEAPV) includes several articles on building applied photovoltaics (BAPV).…”

Determinant factors in site selection for photovoltaic projects: A systematic review

“…The review study was conducted by Kaya et al where they sought to analyze the different methods to aid the decision‐making process in the energy area. Lee et al have developed an integrated multiobjective optimization model that finds the ideal solar incentive project from the perspective of the government and the photovoltaic investor, with the intention to increase the benefits of this investor by reducing incentives for government. Jin et al developed an interactive fuzzy chance–constrained resolution method to endorse the planning of energy systems under conditions of uncertainty, making possible a quantitative analysis of the effects of different policies, promoting the sustainable development of energy systems.…”