Hybrid renewable energy system (HRES) is a nexus of several renewable energy systems that concertedly overcomes the limitations of individual systems operating in isolation. This concept of HRES is extended in this study by considering the network of several such HRESs which further improves the flexibility in overcoming the individual limitations. However, operating such a network of HRESs involves complex decision-making and requires efficient optimization. In this study, a novel hierarchical two-tier optimization methodology for a network of HRESs is proposed when they trade power with each other and with the macro grid. In the proposed methodology, the operational optimization of each HRES is carried out by minimizing the operating cost of various sub-units in the first layer. In the second layer, three cooperative optimization strategies inspired from concepts of double-auction e-marketing are applied when HRESs are assumed to operate under the grand coalition. These three cooperative strategies try to achieve the following objectives (i) minimization of power loss during local trade, (ii) maximization of total market gain, and (iii) simultaneous power loss minimization and total market gain maximization, respectively. Simulation results depict that the cooperative optimization approach yields a reduction in power loss up to 7 % and improvement in market utility up to 29% relative to a scenario when they operate non-cooperatively.

混合可再生能源系统 (HRES) 是几个可再生能源系统的纽带，它们协同克服了单独运行的单个系统的局限性。本研究通过考虑几个此类 HRES 的网络扩展了 HRES 的概念，这进一步提高了克服个体限制的灵活性。然而，运行这样一个 HRES 网络涉及复杂的决策，需要有效的优化。在这项研究中，当 HRES 相互之间以及与宏电网进行电力交易时，提出了一种用于 HRES 网络的新型分层两层优化方法。在所提出的方法中，通过最小化第一层中各个子单元的运行成本来执行每个 HRES 的运行优化。在第二层，当假设 HRES 在大联盟下运作时，将应用三种受双重拍卖电子营销概念启发的合作优化策略。这三种合作策略分别试图实现以下目标：(i) 本地交易期间的功率损耗最小化，(ii) 总市场收益最大化，以及 (iii) 同时功率损耗最小化和总市场收益最大化。仿真结果表明，与非合作运行的情况相比，合作优化方法可将功率损耗降低多达 7%，并将市场效用提高多达 29%。这三种合作策略分别试图实现以下目标：(i) 本地交易期间的功率损耗最小化，(ii) 总市场收益最大化，以及 (iii) 同时功率损耗最小化和总市场收益最大化。仿真结果表明，与非合作运行的情况相比，合作优化方法可将功率损耗降低多达 7%，并将市场效用提高多达 29%。这三种合作策略分别试图实现以下目标：(i) 本地交易期间的功率损耗最小化，(ii) 总市场收益最大化，以及 (iii) 同时功率损耗最小化和总市场收益最大化。仿真结果表明，与非合作运行的情况相比，合作优化方法可将功率损耗降低多达 7%，并将市场效用提高多达 29%。