Stochastic planning of electricity and gas networks: An asynchronous column generation approach

Coordinated Operation of Electricity and Natural Gas Networks with Consideration of Congestion and Demand Response

“…The price-elastic load in the electricity network is based on the model reported in References [54,55]. Equations ( 27)-( 30) represent electrical load deviation and electricity price deviation in the demand response program.…”

Robust Co-Optimization Scheduling of Electricity and Natural Gas Systems via ADMM

“…Se puede evidenciar que, con la consideración de las restricciones de gas natural, el suministro del combustible se ve afectado para la generación eléctrica y cambian los precios marginales. Adicionalmente, [52] propone un modelo para el planeamiento de la red de electricidad y gas mediante un enfoque estocástico multietapa que se resuelve a través de una descomposición novedosa de Dantzing-Wolfe a la vez que propone una linealización de flujo de gas por los ductos y detalla el modelado de los compresores proporcionando un modelo más realista que mejora la calidad de las decisiones de inversión. Por su parte, [53] propone una estrategia para alivianar la alta dependencia a la red de gas natural, para ello realiza un análisis para el corto plazo que incluye la respuesta en demanda y hace uso de una aproximación lineal de las restricciones mencionadas en el espacio euclidiano tridimensional.…”

Optimal planning and operation of multi-vector energy networks: A systematic review

“…A summary of the popular methods used by the research community for problem formulation and problem solving for optimal planning of IENs is presented in Table 5. CONOPT solver in GAMS [158] Branch-and-Reduce Optimization Navigator solver (BARON) in GAMS [159,166,169,172,179] Evolutionary specialised algorithm or Chu-Beasley genetic algorithm [160] Modified differential evolution (DE) algorithm with fitness sharing [163,194] CPLEX solver in GAMS [164,170,173,176,181,182,184,186,187,189,192] Extended Mathematical Programming (EMP) framework in GAMS [166] MATLAB/YALMIP/GUROBI solver [168] DIscrete and Continuous OPTimizer (DICOPT) in GAMS [172] Modified binary particle swarm optimization (BPSO) method [178] Non-dominated Sorting Genetic Algorithm-II (NSGAII) implemented in MATLAB [185] Gurobi [190] DECIS solver in GAMS…”

“…It was found that heat demand needs to be considered in operational planning of the flexibility of IENs [12]. Also, in order to consider the uncertain future demand and generation, stochastic solutions need to be investigated, which present the lowest expected cost for this purpose [176], and hence does not result in any load curtailment [164]. It was also found that demand uncertainty impacts the operational cost and not the investment cost [160].…”