With the emergence of energy communities, where a number of prosumers (consumers with their own energy generation) invest in shared renewable generation capacity and battery storage, the issue of fair allocation of benefits and costs has become increasingly important. The Shapley value, a solution concept in cooperative game theory initially proposed by Nobel prize-winning economist Lloyd Shapley, has attracted increasing interest for redistribution in energy settings. However, due to its high time complexity, it is intractable beyond communities of a few dozen prosumers. This study proposes a new deterministic method for approximating the Shapley value in realistic community energy settings and compares its performance with existing methods. To provide a benchmark for the comparisons of these methods, we also design a novel method to compute the exact Shapley value for communities of up to several hundred agents by clustering consumers into a smaller number of demand profiles. Experimental analyses with large-scale case studies of a community of up to 200 household consumers in the UK show that the newly proposed method can achieve very close redistribution to the exact Shapley values but at a much lower (and practically feasible) computation cost. Furthermore, it performed similarly to the probabilistic, state-of-the-art approximation method while having smaller time complexity as well as other desirable characteristics for cost redistribution in energy communities.

The volatile production of renewable energy sources is often considered a key issue in future sustainable energy systems. Drawing on microgrid research, we argue that the potential synergy between microgrid energy management and provision of flexibility services to the wider grid could be further explored. In addition, the trade-off between cost minimisation and energy autarky of a community microgrid has not yet been fully investigated. This study therefore investigates the role of community microgrids in managing the volatility of renewable energy production in the wider grid, while also considering local optimisation trade-offs. The goal of this study is threefold: (1) identifying possibilities for market interactions, (2) developing simulation models and optimization/decision software, and (3) conducting exploratory experiments. To illustrate our ideas, the community microgrid of Schoonschip was used as a case study. This Schoonschip microgrid contains 30 floating houseboats, that are equipped with the following energy resources: PV panels, batteries, modulating heat pumps, thermal collectors, buffer tanks, underfloor heating systems and electrical boilers. The resources are centrally controlled by an energy management system and the Schoonschip microgrid is connected via an interconnection of 160kVA. Simulation models were created for simulating the energy resources and heat demand of the houses. Optimization/decision software was developed and used for microgrid energy management and control of flexibility services. This thesis provides insights into (1) potential market interactions between a community microgrid and the Dutch electricity wholesale market, (2) the trade-off between maximisation of the forecasted solar self-consumption and minimisation of electricity costs, and (3) the potential value and the problems associated with the provision of grid stability services. This study has resulted in a simulation model and decision software, which can also be used and further extended for additional experimental studies into microgrids and flexibility services.

In contemporary societies, industrial processes as well as domestic activities rely to a large degree on a well-functioning electricity system. This reliance exists both structurally (the system should always be available) and economically (the prices for electricity affect the costs of operating a business and the costs of living). After many decades of stability in engineering principles and related economic paradigms, new developments require us to reconsider how electricity is distributed and paid for. Twowell-known examples of important technological developments in this regard are decentralised renewable energy generation (e.g. solar and wind power) and electric vehicles. They promise to be highly useful, for instance because they allow us to decrease our CO2 emissions and our dependence on energy imports. However, a widespread introduction of these (and related) technologies requires significant engineering efforts. In particular, two challenges to themanagement of electricity systems are of interest to the scope of this dissertation. First, the usage of these technologies has significant effects on howwell (part of) supply and demand can be planned ahead of time and balanced in real time. Planning and balancing are important activities in electricity distribution for keeping the number of peaks low (peaks can damage network hardware and lead to high prices). It can become more difficult to plan and balance in future electricity systems, because supply will partly depend on intermittent sunshine and wind patterns, and demand will partly depend on dynamic mobility patterns of electric vehicle drivers. Second, these technologies are often placed in the lower voltage (LV) tiers of the grid in a decentralised manner, as opposed to conventional energy sources, which are located in higher voltage (HV) tiers in central positions. This is introducing bi-directional power flows on the grid, and it significantly increases the number of actors in the electricity systems whose day-to-day decisionmaking about consumption and generation (e.g. electric vehicles supplying electricity back to the network) has significant impacts on the electricity system. In this dissertation, we look into dynamic pricing and markets in order to achieve allocations (of electricity and money) which are acceptable in future electricity systems. Dynamic pricing and markets are concepts that are highly useful to enable efficient allocations of goods between producers and consumers. Currently, they are being used to allocate electricity between wholesale traders. In recent years, the roles of the wholesale producer and the retailer have been unbundled in many countries of the world, which is often referred to as “market liberalisation”. This is supposed to increase competition and give end consumers more choice in contracts. Market liberalisation creates opportunities to design markets and dynamic pricing approaches that can tackle the aforementioned challenges in future electricity systems. However, they also introduce new challenges themselves, such as the acceptance of price fluctuations by consumers. The research objective of this dissertation is to develop market mechanisms and dynamic pricing strategies which can deal with the challenges mentioned above and achieve acceptable outcomes. To this end, we formulate three major research questions: First, can we design pricing mechanisms for electricity systems that support two necessary featureswell, which are not complementary—namely to encourage adaptations in electricity consumption and generation on short notice (by participants who have this flexibility), but also to enable planning ahead of electricity consumption and generation (for participants who can make use of planning)? Second, the smart grid vision (among others) posits that in future electricity systems, outcomeswill be jointly determined by a large number of (possibly) small actors and allocations will be mademore frequently than today. Which pricing mechanisms do not require high computational capabilities from the participants, limit the exposure of small participants to risk and are able to find allocations fast? Third, automated grid protection against peaks is a crucial innovation step for network operators, but a costly infrastructure program. Is it possible for smart devices to combine the objective of protecting network assets (e.g. cables) from overloading with applying buying and selling strategies in a dynamic pricing environment, such that the devices can earn back parts of their own costs? In order to answer the research questions, our methods are as follows: We consider four problems which are likely to occur in future electricity systems and are of relevance to our research objective. For each problem, we develop an agent-based model and propose a novel solution. Then, we evaluate our proposed solution using stochastic computational simulations in parameterised scenarios. We thus make the following four contributions: In Chapter 3,we design a market mechanism in which both binding commitments and optional reserve capacity are explicitly represented in the bid format, which can facilitate price finding and planning in future electricity systems (and therefore gives answers to our first research question). We also show that in this mechanism, flexible consumers are incentivised to offer reserve capacity ahead of time, whichwe prove for the case of perfect competition and showin simulations for the case of imperfect competition. We are able to show in a broad range of scenarios that our proposed mechanism has no economic drawbacks for participants. Furthermore (giving answers to our second research question), the mechanism requires less computational capabilities in order to participate in it than a contemporary wholesale electricitymarket with comparable features for planning ahead. In Chapter 4, we consider the complexity of dynamic pricing strategies that retailers could use in future electricity systems (this gives answers to our first, but foremost to our second research question). We argue that two important features of pricing strategies are not complementary—namely power peak reduction and comprehensibility of prices—and we propose indicators for the comprehensibility of a pricing strategy from the perspective of consumers. We thereby add a novel perspective for the design and evaluation of pricing strategies. In Chapter 5, we consider dynamic pricing mechanisms where the price is set by a single seller. In particular, we develop pricing strategies for a seller (a retailer) who commits to respect an upper limit on its unit prices (this gives answers to both our first and second research question). Upper price limits reduce exposure of market participants to price fluctuations. We show that employing the proposed dynamic pricing strategies reduces consumption peaks, although their parameters are being simultaneously optimised for themaximisation of retailer profits. In Chapter 6, we develop control algorithms for a small storage device which is connected to a low voltage cable. These algorithms can be used to reach decisions about when to charge and when to discharge the storage device, in order to protect the cable from overloading as well as to maximise revenue from buying and selling (this gives answers to our third research question). We are able to show in computational simulations that our proposed strategies perform well when compared to an approximated theoretical lower cost bound. We also demonstrate the positive effects of one of our proposed strategies in a laboratory setupwith real-world cable hardware. The results obtained in this dissertation advance the state of the art in designing pricing mechanisms and strategies which are useful for many use cases in future decentralised electricity systems. The contributions made can provide two positive effects: First, they are able to avoid or reduce unwanted extreme situations, often related to consumption or production peaks. Second, they are suitable for small actors who do not have much computation power but still need to participate in future electricity systems where fast decision making is needed.@en

Electricity consumption is highly correlated with the level of human development, which alongside electrification is expected to significantly increase global demand for electricity in the coming decades. In current electricity systems, most of the electricity is generated by large fossil-fuel power plants on-demand and it is distributed by centrally-managed electricity grids. The increasing demand for electricity, however, should not go hand in hand with the simultaneous intensification of fossil-fuel mine and use, which is a driving cause of rising average temperatures on Earth’s surface. Natural sources such as the sun and wind are expected to replace conventional sources of electricity, such as coal and gas power plants, in the near future, providing a key measure to address climate change and abate the effects of global warming. However, the intermittent and distributed nature of renewable electricity sources requires a redesign of conventional electricity grids that were originally designed following a top-down approach.@en