Energy as a service (EaaS) is an emerging business model that enables the otherwise passive energy consumers to play an active role and participate in the energy utility services. This platform is formed through smart contracts registering peer-to-peer (P2P) transactions of energy through price and quantity. Many industries, including finance, have already leveraged smart contracts to introduce digital currencies. At this time, the utility industry is faced with the challenge of how to structure smart contract formation in a local energy market. Specifically, they are faced with the challenge of maintaining a balance between energy generation and demand while enabling traceability, security, and unbiased peer-to-peer energy transactions, especially within a virtual power plant. This article aims at addressing the aforementioned challenges. In particular, this article investigates how to structure the microgrids in a local energy market, and how to ensure balance and resiliency with incomplete information. Taking various generation asset dimensions and demand profiles into account, simulations are performed. A novel evolutionary computing strategy to structure the simulation is proposed. A comparison is made among random order, random selection, profit-based ranking, and evolutionary strategy for coordinating the contract formation. The discussions draw attention to each method's advantages and disadvantages in terms of their value as a strategy for forming smart contracts in a local energy market.

Summary: The portfolio of virtual power plants (VPP) is a flexible system for facilitating a wide range of resources with wide geographical coverage. A VPP can be placed at the intersection of electric power and energy systems by adding heat pumps to the portfolio, thereby accelerating the formation of integrated energy systems. VPPs, while virtual, still rely on a physical network for operations. The power transmission network has the responsibility of ensuring the security of supply, reliability of operation, and optimal planning for expansion. A vast majority of power network infrastructures, such as lines and towers, were installed in the last century, thus the existing infrastructure with aging components is not homogeneous. The transmission network covers a large geographical area which is expensive to maintain and requires large investments to avoid congestion. This work addresses the research question of how the condition of the existing network infrastructure affects the potential plans to expand the network. The condition of network infrastructure refers to the health of each line and tower. The decision of expansion includes both expansion with dismantling (restructuring) and without (reconfiguration) dismantling the existing lines and towers. The condition of the power network is determined by the maintenance cost of lines as well as a health index with associated risk factors of the tower. The investigation covers a range of cases where we research how the investment decisions change with the option of installing heat pumps in order to meet heating demand locally, and also when the condition of the network (health of lines and towers) is factored in, while also adding in the level of risk associated with each tower. Beyond the cases, extensive sensitivity analyses are conducted to evaluate the trade‐offs between decision variables, such as cost of heat pump, coefficient of performance of heat pump, risk factors, and line and tower costs. The results of this work lay the foundation for understanding how the condition of network infrastructure impacts optimal decisions for expansion.