• Energy Research
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Due to the increasing penetration of photovoltaic (PV) systems, electric vehicles (EV) and other smart devices on a household level, the role of consumers changes from pure consumption to production and storage of electricity. These prosumers will also directly participate in future electricity markets. To compensate for the small scale and the fluctuations in their demand and production, one promising approach for prosumers is to form small energy communities or microgrids, and participate in the electricity markets as one entity. A challenge for these microgrids is to find an optimal energy management strategy, mainly due to the uncertainty in electricity prices, in PV generation as Well as in the prosumer loads. To integrate this uncertainty into the planning, an adaptive robust optimization approach using linear decision rules is proposed in this paper. The linear decision rules allow for a delayed determination of some of the decisions and can therefore adapt to realizations of the uncertainty. Three different uncertainty scenarios are used to evaluate and compare the proposed approach in a case study and to get more structural insights into the efficiency of the approach.

Our energy supply chain is changing rapidly, driven by a societal push towards clean and renewable resources. However, these resources are often uncontrollable (e.g., wind and sun) and are increasingly being exploited on smaller scales (e.g., rooftop photovoltaic). This poses a reliability challenge for the operation of our energy supply chain, specifically for our electricity grid. In this grid, supply and demand must be matched at all times, since storage is virtually on-existent. Traditionally, the supply is controlled centrally and follows the load, where the latter is assumed to be uncontrollable. With the growing number of uncontrollable distributed renewable resources in the system, the centralized paradigm is quickly becoming infeasible. To combat the decreasing flexibility due to loss of controllability on the generation side, often the exploitation of flexibility on the consumption side is considered. This flexibility comes from devices that can adapt their energy use, e.g., smart white goods or electric vehicles (EVs) with smart chargers. Such resources of flexibility on the consumer side are called distributed energy resources (DERs). With the expected growth of the number of DERs in future energy systems, their coordination offers potential to operate the grid more efficiently and allows the integration of more (uncontrollable) energy from renewables into the grid. Traditional steering approaches in the electricity grid do not scale well with the number of DERs and were not designed for the diversity (i.e., heterogeneity) of the envisioned DERs. Thus, new energy management approaches are required. In this thesis we introduce and study such an energy management approach called profile steering. The profile steering approach decentralizes (part of) the computational effort to ensure scalability, making it a decentralized energy management approach. Profile steering relies on predictions and scheduling,meaning that it predicts the future system state and requirements and schedules the use of flexibility of the available DERs to best meet the system goals. We focus on the distribution grid, as a large part of the DERs are expected to be present in this part of the grid.The profile steering approach influences the energy use of DERs using generic steering signals. We show that the approach can incorporate a broad class of such steering signals. This implies that the approach is flexible enough to be applied in many different situations. Furthermore, we exploit the hierarchical structure of the electricity grid to set up a corresponding hierarchical control structure. This structure allows us to incorporate local limitations into our approach, for instance maximum cable loading of the considered grid section.As the developed approach is decentralized, we distribute (part of) the required computation to a local level, i.e., to a controller inside a home or embedded in a device. Such controllers often do not have large computational power. To ensure the computations can be feasibly executed on these local controllers with low computational power the resulting distributed scheduling problems have to be researched. We show that many of these problems fall into the class of resource allocation problems, which are well studied in literature. Several of these problems are extensions of known problems.Therefore, we apply some of the techniques found in literature and extend them to include common cases (current and futuristic) in residential energy settings.In particular we consider buffering devices. Such buffering devices can utilize an internal buffer to decouple (part of) the time they require energy for their operation and the time this energy is taken from the grid.The first type of such a device we consider is the electric vehicle (EV). Scheduling the energy use of the EV is similar to a classical resource allocation problem if it can charge at any rate between zero and a given maximum. To solve this case we apply techniques from literature. However, if the EV can only be charged at a finite number of rates, the problem becomes NP-hard, even if we are only interested in obtaining feasible solutions. To circumvent this issue we consider an adaptation of the problem for which we develop an efficient solution method giving results that are nearly identical to feasible solutions to the original problem.In a follow up chapter we extend the results found for the EV to devices that also allow discharging, e.g., residential stationary batteries and EVs with vehicle-to-grid capabilities. Furthermore, we study heating, ventilation, and air conditioning systems as a special case. In these systems the energy losses depend on the energy present in the storage (in this case the house itself). Next to developing a method to control such a device, we also study the effect of prediction errors on our approach and show that we can effectively deal with them in the case of heating, ventilation, and air conditioning systems using an approach inspired by model predictive control.We use simulations to show the validity of profile steering using several cases. We show that profile steering can also be used to achieve near optimal results when minimizing the degradation of a power transformer. This indicates that the benefits that can be expected from using our approach are not limited to energy markets, but also include increased lifetime of grid assets resulting in reduced investment costs in these assets.Summarizing, the introduced profile steering decentralized energy management approach promises to be a valuable approach in the future (smart) electricity grid where it can unlock the potential of many residential DERs and assist in an effective and efficient energy transition.

Domestic renewable energy systems, including photovoltaic energy generation, as well as local storage, are becoming increasingly popular and economically feasible, but do come with a wide range of options. Hence, it can be difficult to match their specification to specific customer’s needs. Next to the usage-specific demand profiles and location-specific production profiles, local energy storage through the use of batteries is becoming increasingly important, since it allows one to balance variations in production and demand, either locally or via the grid. Moreover, local storage can also help to ensure a continuous energy supply in the presence of grid outages, at least for a while. Hybrid Petri net (HPN) models allow one to analyze the effect of different battery management strategies on the continuity of such energy systems in the case of grid outages. The current paper focuses on one of these strategies, the so-called smart strategy, that reserves a certain percentage of the battery capacity to be only used in case of grid outages. Additionally, we introduce a new strategy that makes better use of the reserved backup capacity, by reducing the demand in the presence of a grid outage through a prioritization mechanism. This new strategy, called power-save, only allows the essential (high-priority) demand to draw from the battery during power outages. We show that this new strategy outperforms previously-proposed strategies through a careful analysis of a number of scenarios and for a selection of survivability measures, such as minimum survivability per day, number of survivable hours per day, minimum survivability per year and various survivability quantiles.

Buildings’ energy consumption accounts for approximately 35% ofemissions in most industrialized countries. In spite of severalstudies on the economy of energy management in buildings, theenvironmental aspect has often been overlooked. Therefore, in thecontext of decarbonization, we investigate the potential for smarthomes to lower their CO2 footprint while saving on their energybills. We model a smart home as a multi-energy system equippedwith several technologies to satisfy both electric and thermal demands.A home energy management system (HEMS) coordinatesthe supply and demand of energy carriers by shifting consumptionin time and by changing energy vectors based on dynamic energyprices and marginal CO2-emission intensities. The HEMS aims atreducing daily CO2 emissions and/or energy costs preserving user’ssatisfaction. Due to the binary nature of on-off decisions and informationuncertainty, we formulate a multi-objective mixed-integerlinear programming (MILP) problem within a model predictive control(MPC) framework. Using prices and CO2-emission intensitiesof the German power grid, our approach is effective in reducingboth energy costs and CO2 emissions, balancing between the twoobjectives. The results show that integrating energy carriers has ahigher impact than time-flexible loads. If solar panels are available,emissions and costs strongly depend on the importance given bythe users to the environmental and economic goals.

The aim of this paper is to investigate households’ willingness to adopt technological and behavioral energy savings measures, in their dwellings and for daily mobility. Based on the evidence that occupants’ behavior has a major impact on energy uses at home and on the road, this paper aims at investigating which determinants influence household preferences for energy-saving measures, both technical as well as behavioral ones, as well as highlighting the key determinants for adopting energy-savings measures, at the household scale. This paper will attempt to shed more light on the factors that may bridge the intention−behavior gap. The analysis is based on an empirical survey carried out in the Netherlands. Main results show that (1) behavioral energy saving measures are more acceptable than technical ones; (2) the adoption of energy savings measures at home is more likely than on the road; (3) there is a relatively small market for technical energy measures, especially through the initial investment and the low return on investment; (4) environmental aspects seem to be more important for relatively expensive technical energy measures; (5) the reason for taking technological energy measures is rather to be found in differences among socio-demographic background than in environmental concerns; and (6) comfort at home and on the road is an important explanatory variable that many respondents used to justify not implementing energy savings measures and should be investigated in further research.

Electrification of processes and utilities is considered a promising option towards the reduction of greenhouse gas emissions from the chemical industry. Therefore, electricity demand is expected to increase steeply. Since the sources of future low-carbon electricity are variable in nature, there is a need for strategies to match availability and demand. Literature identified the flexibility of chemical processes as one promising strategy to address variability. This study aims to provide insights into how stakeholders from the power sector and the chemical industry consider flexibility in chemical processes and to identify key benefits and bottlenecks. For this article, we combined a review of peer-reviewed and grey literature with stakeholder interviews to map and describe the state of the art of flexible chemicals production, and to identify requirements for further research. The main drivers to investigate the flexibility potential are first, the contribution to energy system reliability, and second, potential cost savings for the industry. Main limitations are considered to be first, the uncertain economic performance of flexible processes due to investment costs, reduced production and uncertain revenues from flexible operation, and second, the complexity of the implementation of flexibility. Energie and Industrie Intelligent Electrical Power Grids

We study a convex quadratic nonseparable resource allocation problem that arises in the area of decentralized energy management (DEM), where unbalance in electricity networks has to be minimized. In this problem, the given resource is allocated over a set of activities that is divided into subsets, and a cost is assigned to the overall allocated amount of resources to activities within the same subset. We derive two efficient algorithms with [Formula: see text] worst-case time complexity to solve this problem. For the special case where all subsets have the same size, one of these algorithms even runs in linear time given the subset size. Both algorithms are inspired by well-studied breakpoint search methods for separable convex resource allocation problems. Numerical evaluations on both real and synthetic data confirm the theoretical efficiency of both algorithms and demonstrate their suitability for integration in DEM systems.