The power generated from wind is not synchronized to the electrical frequency of the power grid. Grid balancing services must be assured when the output from a wind farm is integrated into the electrical grid to avoid the risk of blackouts. Active Power Control (APC) methods are employed to provide grid balancing ancillary services such as frequency control. One objective of APC is to have the total power generated from a wind farm, track the power demand requirements obtained from the utility grid. To achieve this objective, the wind farm should be able to operate below their maximum power production capacity i.e, in derating mode. This implies that the turbines in the wind farm should also be derated. The presence of wakes in the wind farm results in the downstream turbines to experience reduced wind speed and increased turbulence.
The earlier works on APC for wind farms revealed the need for a closed-loop wind farm control strategy to combat the effect of wake turbulence. The presence of wakes posed several challenges on obtaining the estimate of the available power at every turbine on a time scale of seconds. Yet, some model-free algorithms were dependent on the estimation of available power at every turbine in the wind farm. This, leads us to the question, “Can a wind farm controller be developed to provide APC for waked wind farms, where the setpoint selection and distribution are made without estimating the available power at each turbine?” To explore the answer to this question, a single wind turbine power tracking control algorithm is developed as the first step. This tracking algorithm does not depend on the estimation of available power. The proposed algorithm makes the turbine operate on two different operating modes namely, the perfect tracking mode and greedy/boosting mode. The algorithms were developed in a way that they can be integrated with the existing torque and pitch controllers of the turbine. Following this, a closed-loop wind farm control strategy has been developed. The closed-loop wind farm controller takes the total power generated from the wind farm as the feedback signal. Based on the operating mode of the individual turbines, the wind farm controller coordinates and distributes the total power reference signal as individual power set-points to the respective wind turbines. The performance of the closed loop wind farm controller was evaluated for a 9-turbine case in SOWFA simulation for four different scenarios. The scenarios differed from each other based on the way the turbines in the wind farm are derated and the individual set points to the turbines are distributed by the wind farm controller. Simulation results showed that the scenario in which the upstream turbines are derated more than the downstream turbines, the tracking performance was better compared to the other scenarios. The damage equivalent loads experienced by the tower base of the individual turbines were also calculated and each of the scenario resulted in different loading patterns. Recommendations are also provided to extend this work and perform further research.

A future rise in electrical energy demand is expected due to the electrification of the thermal energy supply and the rise in popularity of the Electric Vehicle (EV). This rise in the electrical energy demand results in needed investments in the electrical energy infrastructure to prevent congestion at the transformer due to the higher peak of energy transfer between the microgrid and utility grid. Smart control strategies as EV management and Demand Response (DR) programs are used to lower the peak of electrical energy transfer. In this thesis, the focus is on how the introduction of hydrogen will influence the peak of electrical energy transfer between the microgrid and utility grid to reduce future electrical grid investments. The stochastic processes in the microgrid are forecasted with the best-obtained forecasting models. Using a mixed logic dynamical formulation of the hybrid model of the microgrid, different Model Predictive Control (MPC) control strategies are implemented to solve the multi-objective mixed-integer linear programming problem. Microgrids with different levels of hydrogen penetration are compared. It is concluded that the introduction of hydrogen to a future microgrid will reduce the peak of electrical energy transfer, i.e., reduce future investments in the electrical grid. However, it does result in higher overall economic costs due to the high increase in energy import costs. Furthermore, an increase in the degradation of the EVs due to their more intensive use is concluded when introducing hydrogen to the microgrid. Two stochastic MPC methods, scenario- and tree-based MPC are compared to the nominal controller to see if better performance can be obtained for a hydrogen-based microgrid. Better overall performance of the stochastic MPC strategies is obtained in the winter but could not be realized in the summer. Only tree-based MPC shows a reduction in the peak of electrical energy transfer.

Traditional electric power systems with large centralized base load power plants have a limited ability to react rapidly to the high supply variability associated with the increasing deployment of variable and intermittent renewable energy sources (RESs). Furthermore, with current power distribution networks primarily designed for unidirectional power flow, the introduction of reverse power flows by renewable feed-in strategies has been shown to negatively impact grid stability, security and system protection. For residential grid-connected microgrids (MGs) wishing to increase their renewable generation, these issues along with economic considerations often highlight that optimal operation can only be achieved through an increased self-consumption of locally generated renewable energy. Recently, researchers have highlighted that these issues can be addressed by the application of demand side management (DSM). Broadly speaking, these DSM strategies can be considered as programs which attempt to modify flexible user demands in order to achieve objectives such as reduced energy costs or increased RES utilization.

To achieve these optimal energy management goals, this thesis focuses its efforts on the application of hybrid economic model predictive control (EMPC) strategies for the DSM of small to medium sized grid-connected residential MGs, containing both local photovoltaic (PV) generation capabilities and thermal energy storage (TES) devices. In particular, the investigation exploits thermal energy storage properties of switched domestic electric water heaters (DEWHs) to optimally schedule energy demand for the minimization of MG operating costs. By considering the time varying electricity tariffs, it is shown that the implementation of EMPC is able to simultaneously target reduced electricity costs while also encouraging the self-consumption of local PV generation.

Finally, to address the unavoidable presence of uncertainty in domestic hot water (DHW) user demand, the thesis additionally explores the use of stochastic and robust variants of EMPC. By explicitly considering uncertainty, these control frameworks are able to provide more robust system operation. Specifically, the work implements min-max and stochastic scenario-based frameworks, which were shown to drastically reduce the violation of user comfort constraints when compared with their deterministic counterparts.

Renewable energy sources, e.g. solar energy and wind energy, have gained popularity as an alternative means of energy production as they do not reinforce global warming. In addition, more and more electrical appliances (e.g. electric vehicles, induction cookers, and heat pumps) are used as a substitute for appliances that need non-renewable energy sources. This increase in the use of renewable energy resources pushes the electricity grid to its limits due to new induced load peaks. The grid is not designed for these developments and as a result, asset deterioration, higher transport losses, and outages are expected to occur. The most straightforward solution for the distributed system operator, i.e. the operating manager of the distribution network, is to expand the grid. However, grid expansion is a costly operation and there are additional promising methods to decrease grid load peaks, e.g. by using different charging strategies for electric vehicles. The conventional charging strategy for electric vehicles is uncontrolled charging. With uncontrolled charging, the charging of the electric vehicle immediately commences once a connection with the charging pole is established. The smart charging strategy, however, is able to delay the charging moment to a more optimal time instant in view of, e.g. variable electricity prices. The vehicle-to-home (V2H) charging strategy is similar to smart charging, but in addition, the V2H strategy allows the electric vehicle to discharge electricity to power a nearby residential home. This research aims to compare smart charging and V2H charging on their economical effects for their users. The charging strategies are implemented using two control algorithms: a rule-based controller and a model predictive control (MPC) algorithm. The rule-based controller is implemented due to its simplicity and the MPC algorithm is used for its ability to take into account predictions of system related variables, e.g. household loads. The MPC algorithm is implemented with two different forecasts namely, perfect information, i.e. uncertain variables are forecasted perfectly, and certainty equivalent, i.e. uncertain variables are predicted using a persistence forecast model. The persistence forecast model assumes that the future values of an uncertain variable remain equal to the latest measurements, e.g. the solar generation of tomorrow is expected to be equal to that of today. The control problem is non-linear as an electric vehicle behaves differently depending on its status, e.g. driving or charging. The control problem is therefore reformulated into a mixed logical dynamical framework such that it can be solved efficiently using mixed integer linear programming. An extensive comparison in performance for a microgrid case study is done using real data of solar generation, electric vehicles, and household loads for simulation. The results show that the V2H charging strategy can outperform smart charging by reducing both the peak loads and the electricity costs. However, the V2H strategy only gives a minor extra decrease in costs compared to smart charging and the performance of V2H charging is highly dependent on the quality of the forecasts. Therefore, it is concluded that, in practice, smart charging is the most effective charging strategy. Further research is done to investigate whether the microgrid costs, using a smart charging strategy, can be reduced further by taking into account the uncertainty of some variables such as the electricity price and the household load. This is implemented through a scenario-based MPC algorithm due to its ability to incorporate multiple forecasts, i.e. scenarios, for each uncertain variable. Six different scenario generation methods are implemented which are distinguished by two characteristics: the period from which the historical error between the certainty equivalent case and the true realization of the uncertain variable is collected (i.e. yearly, seasonal, or daily) and the addition method of these errors to the persistence forecast model to generate new scenarios, i.e. as a variable or as a constant. An extensive comparison in performance for a microgrid case study is done. The results show that the certainty equivalent MPC case can be outperformed if a low number of scenarios is generated. This is achieved most effectively by collecting the persistence forecast model error from a rolling horizon of the past 24 hours and adding the error as a constant to the persistence forecast model.

Fast depleting fossil fuels and growing awareness for environmental protection have led us to the urgency of a long-term energy planning where reduction of emissions, integration of renewable supply, and energy efficiency improvement represent the main targets of a ‘smarter’ employment of primary resources. Research is needed nowadays to drive a transient phase towards the construction of future ‘smart grids’, where multiple actors will be able to communicate with each other and efficiently adapt their production/consumption with respect to the dynamic evolution of the increasingly complex power network. In this scenario, operational management of small, local electricity networks (microgrids) and their two-way interconnection to the main grid are creating new opportunities and, at the same time, new technological challenges. Advanced control schemes are being investigated to smoothen the integration of distributed generation and to achieve optimal operation at microgrid level, through coordination and dispatching of power generation, flexible loads, and storage elements.
The residential sector is responsible for about 30% of the global energy consumption and has historically played a passive role in the unidirectional centralised power infrastructure. A residential microgrid that utilises controllable prime movers, such as gas engines, to compensate fluctuating demand and output of renewable energy would represent a fundamental step towards a more economic, efficient, and environment friendly energy infrastructure. This MSc thesis project focuses on the design of energy management systems in residential buildings where micro-Combined Heat and Power (CHP) generators are installed. Micro-CHP technology is able to produce electrical energy locally in a controllable way, having at the same time the advantage of efficiently employing by-product heat to satisfy thermal demand of the building where it is located. The purpose of our work is an economic analysis regarding the profitability of investment in distributed energy resources for Dutch households and a subsequent investigation about the benefits that advanced control techniques would lead to microgrid operation on the long run. For this reason, specific case studies are built based on real data of thermal and electric consumption, which have been collected through smart meters in various Dutch houses. Two different versions of the microgrid are considered: a first case only involves micro-CHP and thermal energy storage, whereas a second one is expanded to include solar panels.
Advanced techniques employed for supervisory control of power flows in microgrids generally aim to take into account relevant information about the consequences of choosing specific actions, by considering future predictions of system evolution. Model Predictive Control (MPC) is a well-known, established and widely used control technique that is often considered as a natural approach to adopt in microgrids. Its main strength is the ability to turn a control problem into an optimisation problem; therefore the capability of including operational constraints arises naturally. However, high volatility of small-scale demand and intrinsic stochasticity of renewable energy supply lead to the hard challenge of integrating appropriate forecasting models into the decision-making strategy. When deterministic approaches relying on the certainty equivalence paradigma are applied in residential microgrids, frequent violations of thermal comfort constraints occur due to poor prediction accuracy of the stochastic
processes involved. The possibility to explicitly take into account the uncertainty affecting the controlled system extends the effectiveness of the predictive control strategies, at the cost of increased complexity. Therefore, suitable probabilistic formulation of the forecasting models for stochastic processes and subsequent control strategies in the MPC framework are studied in our work. Different stochastic approaches recently studied in the scientific literature, i.e. scenario based and tree based, are implemented and compared for the defined case studies. Their performance is evaluated in terms of economic savings, primary energy consumption, and violation of thermal comfort constraints for the households.
The results of our work show the profitability of investment in residential microgrids for average Dutch households willing to share the installation of distributed energy resources in multifamily buildings, even in absence of government subsidies. Moreover, the employment of predictive strategies for local generation scheduling results in slightly improved performance with respect to traditional rule-based controllers. The poor prediction accuracy of demand forecasting on small spatial scale still represents the main difficulty to overcome in order to fill the gap with the theoretical potential benefits of ‘optimal’ predictive strategies. However, in the investigated context, the need for a stochastic framework is motivated and highlighted
with respect to the usage of deterministic tools due to the large variance of uncertainty in system dynamics.