The desire to overcome reliability issues of distributed energy resources (DERs) lead researchers to development of a novel concept named as virtual power plant (VPP). VPPs are supposed to carry out intelligent, secure, and smart energy trading among prosumers, buyers, and generating stations along with providing efficient energy management. Therefore, integrating blockchain in decentralized VPP network emerged out as a new paradigm, and recent experiments over this integration have shown fruitful results. However, this decentralization also suffers with energy management, trust, reliability, and efficiency issues due to the dynamic nature of DERs. In order to overcome this, in this paper, we first work over providing efficient energy management strategy for VPP to enhance demand response, then we propose an energy oriented trading and block mining protocol and named it as proof of energy market (PoEM). To enhance it further, we integrate differential privacy in PoEM and propose a Private PoEM (PPoEM) model. Collectively, we propose a private decentralized VPP trading model and named it as Virtual Private Trading (VPT) model. We further carry out extensive theoretical analysis and derive step-by-step valuations for market race probability, market stability probability, energy trading expectation, winning state probability, and prospective leading time profit values. Afterwards, we carry out simulation-based experiment of our proposed model. The performance evaluation and theoretical analysis of our VPT model make it one of the most viable model for blockchain based VPP network as compared to other state-of-the-art works.
翻译:由于希望克服分配能源(DERs)的可靠性问题,研究人员因此发展了一个名为虚拟发电厂(VPP)的新概念。 VPP应该是在制造者、买主和发电站之间开展智能、安全和智能的能源交易,同时提供高效的能源管理。因此,将分散化的VPP网络中的供应链整合为一个新的范式,最近关于这种整合的实验也取得了丰硕的成果。然而,这种权力下放还由于能源管理、信任、可靠性和效率问题而受到影响,因为DERs具有动态的性质。为了克服这一点,我们在本文件中首先努力为VPPP提供高效的能源管理战略,以加强需求回应,然后我们提出以能源为导向的交易和封建采矿协议,并将其命名为能源市场的证明。为了进一步加强这一点,我们把分散化的VPPP网络的隐私差异化作为一个新的范例,我们提出了私营分散化的VPPP交易模式,并把它命名为虚拟私营贸易(VPT)模式。我们进一步进行了广泛的理论分析,并逐步评估VPPPP公司(VPT)公司(VPPT)的模型,目的是加强需求。然后,我们提出了一个市场竞争潜力交易前景的概率分析。