The intensified interactions between power and gas systems and the wide utilization of renewable energy introduce additional challenges in energy marketing and pricing mechanisms. Existing studies either neglect energ...
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The intensified interactions between power and gas systems and the wide utilization of renewable energy introduce additional challenges in energy marketing and pricing mechanisms. Existing studies either neglect energy uncertainties in deterministic market-clearing models, use predetermined strategic offering prices single-level models, or approximate gas dynamics in nonrealistic models. This paper proposes a bi-level two stage distributionally robust electricity-gas market clearing (EG-Mc) model considering energy uncertainties and strategic offering prices from energy producers. Strategic energy producers submit their offering prices in the upper-level problem to the EG-Mc operator, who maximizes market profits under the realizations of renewable energy outputs while balancing the robustness and conservativeness of the day-ahead market decisions. The presence of gas dynamics in the two stages of the decision-making framework generates an intractable EG-Mc problem. A novel triple-loop procedure, namely inner and outer columns & constraints generation and bilinear approximation algorithms, is proposed to sufficiently solve the formulated model. Finally, numerical analyses on an EG-Mc model demonstrate the effectiveness of the distributionally robust strategic offers and the performances of the solution methodology.
The hybrid energy system of hydro-powers, pumped storages and renewable energies has become a new topic direction in modern power system developments. consequently, it is essential to realize a rational and efficient ...
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The hybrid energy system of hydro-powers, pumped storages and renewable energies has become a new topic direction in modern power system developments. consequently, it is essential to realize a rational and efficient allocation of different energy source capacities. Nevertheless, there is still a gap between the available studies and the requirement for further hybrid energy system development. This paper focuses on the optimal capacity configuration of a wind, photovoltaic, hydropower, and pumped storage power system. In this direction, a bi-level programming model for the optimal capacity configuration of wind, photovoltaic, hydropower, pumped storage power system is derived. To model the operating mode of a pumped storage power station, two 0-1 variables are introduced. To handle the nonlinear and nonconvex lower level programing problem caused by the two 0-1 variables, it is proposed that the 0-1 variables are treated as some uncertain parameters. Also, by treating the 0-1 variables as some uncertain parameters, a two-stage robust optimization problem to decompose the original bi-level programing one into a master problem and a subproblem is finally introduced. The Karush-Kuhn-Tucker (KKT) conditions are then applied to simplify and linearize the min-max problem and nonlinear terms in the master problem. This results in both the master problem and the subproblem being formulated as mixed integer linear programming (MILP) problems. By utilizing the powerful column-and-constraint Generation (c&cg) algorithm, the two-stage robust optimization model is decomposed into an iterative procedure of solving the master problem and the subproblem sequentially. This approach eliminates the need for intricate optimization algorithms as commonly used in existing bi-level planning problems in hybrid energy systems. Finally, the effectiveness and advantages of the proposed model is verified by the numerical results on a case study.
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