On Optimization Based Control of Distributed Energy Resources in Power Systems: Electricity Markets and Voltage Regulation Perspectives
Doctoral thesis, 2016
Distributed energy resources are increasingly being integrated into power systems around the world. These resources, typically connected at the distribution system level, are already changing the pattern of power flow from being unidirectional to multidirectional. Treating them as negative loads would only limit their further integration and could add to grid investment costs, while optimally controlling their output could potentially reverse that position. The latter approach is of significance to electricity markets and stakeholders that trade electrical energy as a commodity, as well as distribution systems and their operators, whose primary responsibility is to deliver electricity reliably and securely to customers. This dissertation deals with modeling and investigating the effects of price-based scheduling of aggregated electric vehicle energy for electricity markets and their coordinated control along with other distributed energy resources for voltage control in electrical distribution systems.
Investigations from an economic perspective include the effects on electricity prices in day-ahead and regulating power markets, impact on retailer's profits, electricity pricing to variable and fixed retail contract customers and associated savings in charging cost for vehicle owners. For this purpose, an electric vehicle aggregator model is developed under day-ahead, regulating power and retail market paradigms. Results show that electrifying all vehicles in the Nordic area and performing price-based control of charging could lead to flattening of day-ahead electricity market price curve when compared to simpler charging strategies that could lead to higher peak prices over the day. In a regulating power market, the aggregator was found to potentially help the system during up-regulation by reducing active power losses due to local production, also reducing the corresponding cost for balancing the power system during operational hour. An electricity retailer performing a two-stage stochastic planning while accounting for price-responsive scheduling by an aggregator could potentially increase their profits with greater number of electric vehicle customers. For the latter, the charging cost savings were found to be higher when more than 30% of the customers opt for variable retail contracts, while for the remaining consumers, the price for fixed retail contracts was found to increase by up to 2 Euros per MWh.
Technical issues investigated include the adoption of centralized and distributed model predictive control-based coordinated voltage regulation using distributed energy resources and their comparison in terms of reactive power utilized and influence on active power loss within a network. Distributed resources considered were solar photovoltaics and battery energy storage systems that could be controllable due to power converter interfacing with the grid. Results from case studies indicate that the developed centralized and distributed model predictive control strategies function according to the design criteria and are able to control bus voltages across the distribution network satisfactorily. The proposed distributed model predictive control strategies, while providing potential practical advantages, utilize around 1.3% more reactive power reserves compared to centralized control strategy for voltage control, also resulting in greater active power losses.
Field validation was also performed in this thesis wherein, the reactive power ouput of a voltage source converter was controlled using the developed voltage control algorithm to maintain a remote bus voltage in the distribution system within specified bounds. Results from the tests indicate that the developed control algorithm functions successfully and as intended within a practical setting.
model predictive control and distribution systems
coordinated voltage control
battery energy storage