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Operations and maintenance management for renewable energy (RE) projects has become increasingly important in improving energy system models' precision. Specifically, some elements are uncertain and difficult to predict, such as temperature conditions or system reliability, and these result in more complicated RE projects. Suitable maintenance and insurance policies are vital to reduce risks for RE system operators. This paper formulates a profit model that integrates system reliability, predictive maintenance, and green insurance into electricity pricing and capacity problems. A non-linear optimization solution procedure is proposed to determine the optimal electricity price, capacity, investment plan, predictive maintenance budget, and insurance level while maximizing company profit. The theoretical results indicate that RE systems' increased reliability decreases the insurance level. However, an increase in the insurance level decreases RE project investments. Therefore, companies can attract more investments for RE projects if RE system reliability increases while insurance costs decrease. Additionally, this work not only presents a numerical analysis with examples and a sensitivity analysis to illustrate the model, but also discusses the opportunities this work offers for managers and analysts in practice. [ABSTRACT FROM AUTHOR]

In a microgrid, an efficient energy storage system is necessary to maintain a balance between uncertain supply and demand. Distributed energy storage system (DESS) technology is a good choice for future microgrids. However, it is a challenge in determining the optimal capacity, location, and allocation of storage devices (SDs) for a DESS. This paper proposes a two-stage approach to solve these SD decision-making problems in a microgrid. In the first stage, a continuous approximation approach was used to formulate the SD location, capacity, and renewable energy investment problems from a long-term perspective. In the second stage, the dispatch quantity problems are addressed to minimize the operational cost based on the results of Stage 1. Some examples and real data are used to illustrate the model and solution approaches. The results show that our approach can achieve the cost minimization. The optimal SD location and capacity strategy and the renewable energy investment level are close to the real data obtained from Liu et al. (2018) after 20 runs. • Designing a two-stage approach to solve these SD decision-making problems in a microgrid. • A continuous approximation approach is used to formulate the SD location, capacity, and renewable energy investment problems from a long-term perspective at the first stage. • The dispatch quantity problems are addressed to minimize the operational cost based on the results of Stage 1 at Stage 2. [ABSTRACT FROM AUTHOR]

• Analyze and discuss the company decision-making when banks support to generate renewable energy and demand uncertain. • The repayment ability of the power company depends on sales revenue. • A single newsvendor model is used to determine the optimal renewable energy price, capacity and amount of loan from bank. • Discuss how company responds to the changes of the different situation of energy market. The costs of renewable energy technologies are highly sensitive to financing terms. Power companies are willing to invest in renewable energy generation because of clean and sustainability concerns. However, renewable energy generation poses a potentially high risk, both in generation and consumption. In this study, we consider how a power company makes its decisions if a credit bank willing to lend to it for renewable energy generation. The repayment ability of the company depends on sales revenue under demand uncertainty. We formulate a mathematical model to help the company determine the optimal electricity price, renewable energy capacity, and amount of loan while maximizing the total expected profit. A searching algorithm is proposed to solve the problem. Several numerical examples are provided to illustrate the proposed model and solution approach. Managerially, our study suggests that increasing renewable energy investments through financing institutions requires careful attention to pricing, capacity, and financing policies. [ABSTRACT FROM AUTHOR]