For the future development of an integrated energy system (IES) with ultra-high penetration of renewable energy, a planning model for an electricity-hydrogen integrated energy system (EH-IES) is proposed with the considerations of hydrogen production and storage technologies. In this EH-IES, a reasonable power to heat and hydrogen (P2HH) model with startup/shutdown constraints and a novel model of seasonal hydrogen storage (SHS) are proposed for the first time. To cope with the challenges faced by EH-IES, we use a combination of stochastic and robust optimization approaches to address the generation-load uncertainties, making the planning results robust. The N-1 contingency of crucial devices is further considered to make the system reliable. Additionally, the effects of the annual ratios of different seasons on the optimal allocation and operation of SHS are investigated. The min-max-min model with binary variables is solved by a nested column-and-constraint generation algorithm. Finally, case studies verified the advantages and effectiveness of the proposed model and planning method.

In view of the plurality of ac and dc microgrids connected to the power grid, this paper proposes a bi-level two-stage robust optimal scheduling model for ac/dc hybrid multi-microgrids (HMMs). In this model, the ac/dc HMM system is divided into two interest bodies, called the utility level and supply level, and two-stage robust (also called adaptive robust) optimal scheduling is carried out considering the uncertainties in each level. For the mutual interaction between the two bodies, power constraints and deviation penalties of the interaction lines are introduced in the model to realize bi-level coordinated scheduling and obtain a unified robust dispatch scheme of ac/dc HMMs. The column-and-constraint generation algorithm is used to convert the Min-Max-Min problem of each level into a two-stage mixed-integer linear programming problem, which can be solved quickly and effectively. Case studies verify the rationality and validity of the proposed model and the solving procedure.

Uncertainties from multiple generation resources and loads have introduced tremendous challenges to the optimal dispatch of microgrids. This paper presents a novel two-stage min-max-min robust optimal dispatch model for a representative islanded AC/DC hybrid microgrid that faces uncertainties in renewable energy generation and customer loads. The first stage of the model determines the startup/shutdown state of the diesel engine generator and the operating state of the bi-directional converter of the microgrid. Then, the second stage optimizes the power dispatch of individual units in the microgrid. A new linearized equipment cost model is developed, counting for the degradation of energy storage. The use of this linear model helps maintain the linearity of objective function without compromising the solution accuracy. The column-and-constraint generation algorithm is implemented to efficiently obtain a robust dispatching plan for the microgrid, which minimizes the daily operating cost in the worst-case scenario. A case study and sensitivity analyses further demonstrate the rationale and the unique capability of the proposed model for planning the operation of AC/DC hybrid microgrids.

To cope with the impact of predicted source-load deviations on the optimal dispatch of AC/DC hybrid microgrids at different time scales, this paper develops a multiple-time-scale (MTS) rolling optimal dispatching framework. A novel day-ahead dis-tributionally robust optimization (DRO) model, based on the predicted means, deviations, and confidence probabilities of the source-load power, reduces the conservativeness of the adaptive robust optimization and provides robust day-ahead scheduling plans. The source-load deviations are effectively compensated by an MTS rolling optimization for the intraday dispatch, which adjusts the operating power of the controllable units. Relaxed penalty cost functions and rigid constraints on the state of charge are added to ensure cyclic regulation of the energy stor-age. The superiority and validity of the day-ahead DRO model and the intraday MTS rolling optimization model are verified in case studies.

Power-to-hydrogen systems have the potential for long-term energy storage and diversified terminal utilization, contributing to high renewable integration in power systems. To explore the potential energy arbitrage of integrated power-to-hydrogen systems (IP2HSs) across power systems and hydrogen supply chains, we propose a long-duration robust optimization (LDRO) framework for IP2HSs considering energy, ancillary services, and hydrogen markets. A unified capacity bidding model in the ancillary service market is established for fuel cells and electrolyzers of IP2HSs to simulate the bidding strategy in typical markets. The hybrid hydrogen demand model is based on the Cournot model considering the cost-competitiveness of hydrogen in different sectors. A direct solution method combining the column-and-constraint generation algorithm and strong duality theory is proposed for the LDRO framework with quadratic terms. Uncertainty sets are constructed based on day-ahead prices in typical markets. A case study investigates the feasibility of energy arbitrage and the ability of the LDRO framework to deal with long-duration price uncertainties in 15 markets. The results highlight the advantages of hybrid hydrogen demand and the sensitivity of energy arbitrage by IP2HSs to different parameters.