• PEV storage in multibus scheduling problems

      Momber, Ilan; Morales-España, Germán; Ramos, Andrés; Gómez, Tómas (IEEE Transactions on Smart Grids, 2014)
      Modeling electricity storage to address challenges and opportunities of its applications for smart grids requires inter-temporal equalities to keep track of energy content over time. Prevalently, these constraints present crucial modeling elements as to what extent energy storage applications can enhance future electric power systems' sustainability, reliability, and efficiency. This paper presents a novel and improved mixed-integer linear problem (MILP) formulation for energy storage of plug-in (hybrid) electric vehicles (PEVs) for reserves in power system models. It is based on insights from the field of System Dynamics, in which complex interactions between different elements are studied by means of feedback loops as well as stocks, flows and co-flows. Generalized to a multi-bus system, this formulation includes improvements in the energy balance and surpasses shortcomings in the way existing literature deals with reserve constraints. Tested on the IEEE 14-bus system with realistic PEV mobility patterns, the deterministic results show changes in the scheduling of the units, often referred to as unit commitment (UC).
    • Regulatory framework and business models for charging PEVs: infrastructure, agents, and commercial relationships

      Gómez, Tómas; Momber, Ilan; Rivier, Michel; Sánchez-Miralles, á. (Energy Policy, 2011)
    • Retail pricing: A bi-level program for PEV aggregator decisions using indirect load control

      Momber, Ilan; Wogrin, Sonja; Gómez, Tómas (IEEE Transactions on Power Systems, 2016)
      Charging schedules of plug-in electric vehicles (PEVs) coordinated by an aggregating agent may increase system efficiency of allocating generation, transmission and distribution resources. Decentralized self-scheduling and local charging control appear to be preferred by vehicle manufacturers and PEV drivers who are simultaneously concerned about the longevity and reliability of their energy storage systems. In such a setting, the aggregator would have to determine energy retail prices as means to indirect load control. This paper proposes a mathematical program with equilibrium constraints optimizing the aggregator's decisions. It endogenously determines the profit-optimal price level subject to the cost minimizing charging schedule of the final customers, who are reacting to a combination of retail price signals and distribution use-of-system network charges. This active response follows an affine demand-price relationship, which is individually parametrized for each vehicle by local information of vehicle characteristics and mobility pattern. The proposed program is applied to two cases: 1) a small case study with 3 vehicles, which highlights the model functionality with detailed hourly information per vehicle, 2) a large-scale fleet of 1000 vehicles provides insights on computational burden. Numerical results indicate that adequate competition in the retail market is necessary to limit the aggregator's monopolistic profitability. Finally, sensitivity runs show dependency on the individual's willingness to pay, the cost of alternative fueling opportunities and minimum state-of-charge requirements.
    • Risk averse scheduling by a PEV aggregator under uncertainty

      Momber, Ilan; Siddiqui, Afzal; Gómez, Tómas; Söder, Lennart (IEEE Transactions on Power Systems, 2015)
      Research on electric power systems has considered the impact of foreseeable plug-in electric vehicle (PEV) penetration on its regulation, planning, and operation. Indeed, detailed treatment of PEV charging is necessary for efficient allocation of resources. It is envisaged that a coordinator of charging schedules, i.e., a PEV aggregator, could exercise some form of load control according to electricity market prices and network charges. In this context, it is important to consider the effects of uncertainty of key input parameters to optimization algorithms for PEV charging schedules. However, the modeling of the PEV aggregator's exposure to profit volatility has received less attention in detail. Hence, this paper develops a methodology to maximize PEV aggregator profits taking decisions in day-ahead and balancing markets while considering risk aversion. Under uncertain market prices and fleet mobility, the proposed two-stage linear stochastic program finds optimal PEV charging schedules at the vehicle level. A case study highlights the effects of including the conditional value-at-risk (CVaR) term in the objective function and calculates two metrics referred to as the expected value of aggregation and flexibility.