Future energy systems are expected to rely increasingly more on distributed energy resources (DER). Prosumers that own energy resources such as photovoltaic (PV) generation and energy storage systems, will play a crucial role in the realization of DER’s in future power systems. L
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Future energy systems are expected to rely increasingly more on distributed energy resources (DER). Prosumers that own energy resources such as photovoltaic (PV) generation and energy storage systems, will play a crucial role in the realization of DER’s in future power systems. Limited grid connections due to local electricity congestion make it more difficult for prospective industrially-sized prosumers to find suitable locations for their business operation. Energy resources such as batteries and PV, can be used to fulfil the desired electricity demands while adhering to the congestion related limits. By combining multiple energy resources and controlling them together the operational cost is drastically reduced. This thesis develops a stochastic optimization model that is able to find the optimal dispatch strategy for energy resources while adhering to constraints set by the DSO in a congested area. Also, a deterministic model is developed to be used as a comparison to the stochastic model. A deterministic model is simple and fast but it does not accurately represent the stochastic nature of PV generation, electricity consumption, and market prices. The stochastic model uses a set of different input scenarios for each time step and finds the optimal strategy considering all scenarios. In this thesis two models that consider stochasticity are developed: a stochastic and a robust model. For the robust model, the imposed grid constraints from the Distribution System Operator (DSO) have to be respected at each time step for each scenario and are classified as hard constraints. The model is able to find a optimal strategy for a week in each season when considering 11 scenarios. All predetermined constraints are respected for each simulation and while this makes the strategy reliable it also makes it conservative. For the second stochastic model, the DSO grid supply constraint is designed to be flexible and allow for occasional overshoot. In this case, flexibility is obtained by constraining the statistical distribution of grid power rather than the value at each time step and scenario. The calculated control strategy results in a simulated revenue increase between 3.67% and 4.78% depending on the season. For each season the objective cost reduces relatively more than the occurrence of grid overshoot. The term grid overshoot is used to indicate a situation where the DSO imposed grid limit is exceeded for a time step. The overshoot remains within the predetermined value threshold of 4% for all simulations of the stochastic optimization model.
