Exploring multiple investment strategies for non-utility-owned DGs: A decentralized risked-based approach

The development of distributed generator (DG) and energy market has facilitated the investment in non-utility -owned DGs, leading to the necessity of decentralized optimization and finical risk management due to multiple uncertainties. To cope with these problems, this paper addresses the planning framework of non-utility-owned DGs considering multiple investment strategies, from the perspective of risk and profit. Initially, the autono-mous planning and operation strategy (APOS), and leasing planning and operation strategy (LPOS) are proposed, considering the different ownership of DG investment/operation rights and pricing mechanism. Then the DG planning problem is modeled as the independent decision-making stage of multiple DG investors and the global coordination stage of distribution system operator (DSO). Furthermore, in the DSO coordination problem, to accurately model the real-time uncertainties in DGs, load demand and main grid price, the conditional value at risk (CVaR) is adopted to manage the risk in profit (RIP). The effect of multiple investment strategies on the tradeoff between RIP and expected profit is analyzed. The planning problem is solved by a decentralized opti-mization approach that ensures the privacy protection and autonomous optimization of investors. Finally, results from the case study of the IEEE 33-bus system and IEEE 123-bus system demonstrate the superiority and effectiveness of the proposed method in dealing with the planning problem for multiple DG investors.

Automated summary

This paper addresses the planning framework of non-utility-owned distributed generators (DGs), considering multiple investment strategies from the perspective of risk and profit. The autonomous planning and operation strategy and leasing planning and operation strategy are proposed to deal with the different ownership and pricing mechanism of DG investment/operation rights. The conditional value at risk is adopted to manage the risk in profit, and the decentralized optimization approach is used to solve the planning problem.