LAUSR

Abstract The interdependency across natural gas, power and heating systems is increasingly tightened due to the wide development of cogeneration plants and electrified heating facilities. Multi-energy integration is a prevalent trend and the energy hub, which acts as an intermediary agent between providers and consumers, is expected to play a central role in allocating energy resources more efficiently. However, uncertainties originating from multiple kinds of energy demands challenge the operation of energy hubs and may compromise system efficiency. Energy trading and sharing among individual hubs offer a unique opportunity to increase system flexibility and reduce the cost under demand uncertainty. In this paper, three quintessential schemes for organizing a cluster of energy hubs at demand side, i.e., individual, sharing market, and aggregation, are studied under a stochastic framework with probabilistic load forecasts. First, we perform theoretical analysis and compare their economic efficiencies from a maximum-utility (or minimum-cost) perspective. Utility curves of respective schemes are given, and several important phenomena are revealed from the economic analysis. Then we discuss the concrete decision-making models of energy hubs under the three schemes, taking into account the change of electricity price in response to the total demand, which give rise to bilevel optimization problems and are technically transformed into mixed-integer linear programs. Finally, we conduct numerical experiments, which validate the theoretical outcomes, and reveal that the sharing scheme can achieve nearly optimal efficiency without a central organizer, and hence appears to be a promising direction for future multi-energy systems.