One battery energy storage system (BESS) can provide multiple services to support electrical grid. However, the investment re-turn, technical performance and lifetime degradation differ widely among different services. This paper proposes a novel method for the whole-life-cycle planning of BESS for providing multiple functional services in power systems. The proposed model aims to balance between extending BESS life duration and maximizing its overall revenue by strategically allocates battery capacity for each application over its whole life cycle. Specifically, BESS are initially used in ancillary service market for frequency regulation service; after certain lifetime degradation, the used batteries are transferred in energy arbitrage market for load shifting service. BESS depreciation along with the services is considered to accurately model the battery degradation effect under different marketing rules. The proposed method is tested on a modified IEEE 33-bus radial distribution system. Numerical results demonstrate that the proposed method can achieve higher economic benefits and longer life span than a single application service.

To help the integration of various distributed energy resources (DERs), this paper proposes a transactive approach to the optimal scheduling for prosumers in coupled energy systems. DERs are coordinately operated in the form of a virtual power plant (VPP), which actively participates in the day-ahead and real-time electricity market, as well as the wholesale gas market. In the day-ahead electricity and wholesale gas markets, a VPP aims to maximize expected profits by determining the unit commitments and hourly scheduling of DERs. In the real-time balancing market, a VPP adjusts DER schedules to minimize imbalance costs. In this paper, we address the energy conversions between electric power and gas loads and investigate the interacting operations of gas and power systems. Our simulation results show that hierarchical, coordinated power and gas scheduling can identify more accurate operation plans for coupled transactive energy systems.

Multi-energy rural microgrids (MERMs) hold both economic potential and multi-energy coordination ability, emerging as a promising energy management paradigm in rural areas. In this paper, an energy scheduling method is investigated for a MERM with renewable energy and biomass resources, aiming to satisfy the rural electrical, thermal, natural gas, and irrigation demands economically. Mathematically, biomass flows are formulated by adopting a differential dynamics model of anaerobic biomass fermentation. The irrigation system is accurately formulated by fully taking into account meteorological information such as ambient temperature and precipitation. To handle the uncertainties in precipitation, reservoir inflows, renewable power generation as well as electrical and thermal load demands, a two-stage stochastic optimization method is employed, and the proposed model is then reformulated into a stochastic mixed integer quadratic programming (SMIQP) problem. To mitigate the computational burden arising from integer variables and enhance the solution efficiency, a scenario decomposition algorithm, progressive hedging (PH), is used to decompose the SMIQP into scenario-wise subproblems, which are then solved in parallel. Finally, the simulation results demonstrate the effectiveness of the proposed MERM scheduling method and the efficiency of the PH algorithm.

Increasing multi-energy coordination in the ship necessitates advanced operation strategies to achieve greenhouse gas reduction and energy efficiency improvement in the maritime industry. However, previous research always overlooks onboard heterogeneous energy carriers and ship power distribution networks (SPDN), as well as underwater radiated noise (URN) generated by ship propellers. This will pose a huge threat to the operational safety of the multi-energy ship microgrids (MESMs) and further harm normal marine life. Hence, this paper formulates a coordinated model for a MESM with combined power, thermal, hydrogen, and freshwater flows. First, the joint energy management and voyage scheduling are modeled for the MESM, considering SPDN constraints and URN limits. Then, a copula-based two-stage operation structure with stochastic programming (SP) and rolling horizon (RH) methods is designed to tackle diverse uncertainties from onboard multi-energy loads and renewable energy. Finally, a progressive hedging (PH) algorithm is developed to support distributed calculation and accelerate the solution. Numerical case studies based on a real voyage in the Nordic countries are used to validate the effectiveness and superiority of the proposed model and method.

An optimal bidding model of controllable loads is proposed to minimize the worst-case conditional expectation of electricity purchase cost simultaneously in day-ahead and real-time markets. By reformulating the worst-case conditional value-at-risk (CVaR) constraints, a solvable semi-definite program (SDP) is presented to relax the moment uncertainty of electricity price and simultaneously determine the optimal day-ahead bid and real-time increment/decrement bid.