Keywords
Mixed-Integer linear program (MILP) formulations, plug-in electric vehicles (PEV), reserves, multi-bus unit-commitment (UC), direct load control
PEV storage in multi-bus scheduling problems
Abstract
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. These constraints present crucial modeling elements, which may affect case study results. Hence, correct modeling improves conclusions as to which extent energy storage applications can
enhance future electric power systems’ sustainability, reliability, and efficiency. This paper presents a novel and improved mixedinteger 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 significant changes in the scheduling of the units, often referred to as unit commitment (UC) .
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. These constraints present crucial modeling elements, which may affect case study results. Hence, correct modeling improves conclusions as to which extent energy storage applications can enhance future electric power systems’ sustainability, reliability, and efficiency. This paper presents a novel and improved mixedinteger 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 significant changes in the scheduling of the units, often referred to as unit commitment (UC) .
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. These constraints present crucial modeling elements, which may affect case study results. Hence, correct modeling improves conclusions as to which extent energy storage applications can enhance future electric power systems’ sustainability, reliability, and efficiency. This paper presents a novel and improved mixedinteger 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 significant changes in the scheduling of the units, often referred to as unit commitment (UC) .
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