Reducing Electricity Consumption Peaks with Parametrised Dynamic Pricing Strategies Given Maximal Unit Prices

Demand response is a crucial mechanism for flattening of peak loads. For its implementation, we not only require consumers who react to price changes, but also intelligent strategies to select prices. We propose a parametrised meta-strategy for dynamic pricing and identify suitable strategies for given scenarios through offline optimisation using a population model. We also model an important and novel constraint: a price cap (a maximal unit price) for consumer protection. We show in computational simulations that the maximal unit price influences the peak reduction potential of dynamic pricing. We compare our dynamic pricing approach with a constant pricing approach and show that our approach, used by a profit-optimising seller, is both peak-reducing and equally profitable

Peak reduction in decentralised electricity systems : markets and prices for flexible planning

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

An electricity market with fast bidding, planning and balancing in smart grids

In future energy systems, peaks in the daily electricity generation and consumption are expected to increase. The "smart grid" concept aims to maintain high levels of efficiency in the energy system by establishing distributed intelligence. Software agents (operating on devices with unknown computational capabilities) can implement dynamic and autonomous decision making about energy usage and generation, e.g. in domestic households, farms or offices. To reach satisfactory levels of efficiency and reliability, it is crucial to include planning-ahead of the energy-involving activities. Market mechanisms are a promising approach for large-scale coordination problems about energy supply and demand, but existing electricity markets either do not involve planning-ahead sufficiently or require a high level of sophistication and computing power from participants, which is not suitable for smart grid settings. This paper proposes a new market mechanism for smart grids, ABEM (Ahead- and Balancing Energy Market). ABEM performs an ahead market and a last-minute balancing market, where planning-ahead in the ahead market supports both binding ahead-commitments and reserve capacities in bids (which can be submitted as price functions). These features of planning-ahead reflect the features in modern wholesale electricity markets. However, constructing bids in ABEM is straightforward and fast. We also provide a model of a market with the features mentioned above, which a strategic agent can use to construct a bid (e.g. in ABEM), using a decision-theoretic approach. We evaluate ABEM experimentally in various stochastic scenarios and show favourable outcomes in comparison with a benchmark mechanism

Electrified heat and transport: energy demand futures, their impacts on power networks and what it means for system flexibility

Demand electrification, system flexibility and energy demand reduction (EDR) are three central tenets of most energy system decarbonisation pathways in the UK and other high-income countries. However, their combined impacts on local energy systems remain understudied. Here, we investigate the impact of different UK energy demand future scenarios on the loading of local electricity networks, and the ability of electrified demand to act flexibly in (i) mitigating the need for network reinforcement and (ii) shifting demand around according to variable tariffs reflecting wider system needs. These scenarios are used to drive spatially- and temporally-explicit technology uptake and energy demand modelling for heating and transport in a localised context, for application to a local electricity network. A particular case study energy network in Scotland, representative of many networks in the UK and Northern Europe, is selected to demonstrate the method. On the basis of the presented case study, which considered a typical winter demand day, energy futures based on EDR policies were found on average to reduce evening transformer loading by up to 16%. Further reductions of up to 43% were achieved with flexible smart charging and up to 69% with the use of vehicle-to-grid. Therefore, we find that policies focused on EDR can mitigate the need for reinforcement of electricity networks against the backdrop of demand electrification. However, flexibility in electricity demand contributes a larger difference to a network’s ability to host electrified heat and transport than relying solely on EDR. When used in tandem, policies that simultaneously pursue EDR and electricity system flexibility are shown to have the greatest benefits. Despite these benefits, peak electricity demand is very likely to increase significantly relative to the current baseline. Therefore, widespread reinforcement is required to local electricity networks in the net-zero transition and, accordingly, urgent investment is required to support the realisation of the UK’s legally-binding climate goals

Electrified heat and transport : energy demand futures, their impacts on power networks and what it means for system flexibility

Demand electrification, system flexibility and energy demand reduction (EDR) are three central tenets of most energy system decarbonisation pathways in the UK and other high-income countries. However, their combined impacts on local energy systems remain understudied. Here, we investigate the impact of different UK energy demand future scenarios on the loading of local electricity networks, and the ability of electrified demand to act flexibly in (i) mitigating the need for network reinforcement and (ii) shifting demand around according to variable tariffs reflecting wider system needs. These scenarios are used to drive spatially- and temporally-explicit technology uptake and energy demand modelling for heating and transport in a localised context, for application to a local electricity network. A particular case study energy network in Scotland, representative of many networks in the UK and Northern Europe, is selected to demonstrate the method. On the basis of the presented case study, which considered a typical winter demand day, energy futures based on EDR policies were found on average to reduce evening transformer loading by up to 16%. Further reductions of up to 43% were achieved with flexible smart charging and up to 69% with the use of vehicle-to-grid. Therefore, we find that policies focused on EDR can mitigate the need for reinforcement of electricity networks against the backdrop of demand electrification. However, flexibility in electricity demand contributes a larger difference to a network’s ability to host electrified heat and transport than relying solely on EDR. When used in tandem, policies that simultaneously pursue EDR and electricity system flexibility are shown to have the greatest benefits. Despite these benefits, peak electricity demand is very likely to increase significantly relative to the current baseline. Therefore, widespread reinforcement is required to local electricity networks in the net-zero transition and, accordingly, urgent investment is required to support the realisation of the UK’s legally-binding climate goals

Power and Energy Student Summit 2019: 9 – 11 July 2019 Otto von Guericke University Magdeburg ; Conference Program

The book includes a short description of the conference program of the "Power and Energy Student Summit 2019". The conference, which is orgaized for students in the area of electric power systems, covers topics such as renewable energy, high voltage technology, grid control and network planning, power quality, HVDC and FACTS as well as protection technology. Besides the overview of the conference venue, activites and the time schedule, the book includes all papers presented at the conference

Optimising investments in battery storage and green hydrogen production

Energy systems are undergoing a transition towards low-carbon alternatives, but intermittent renewable sources like wind and solar pose challenges. Battery storage and hydrogen technologies, offer potential solutions with numerous benefits. They can enhance grid stability, improve power quality, and decarbonise industries like heavy manufacturing, heating, and shipping. Both batteries and hydrogen complement renewables by storing excess power and using curtailed energy. To drive the widespread adoption of low-carbon energy technologies, it is crucial to establish its economic viability. This research focuses on optimising the revenues of low-carbon energy investments, specifically battery storage and green hydrogen production. It explores three key areas: determining the optimal usage of these technologies, identifying the best deployment locations, and addressing uncertainties. In terms of usage, the research analyses various case studies and modelling techniques. It applies optimisation models to energy markets, examines community-owned battery projects, and combines machine learning with optimisation models to maximise battery revenues across different market segments. Additionally, the research explores the optimal investment and usage of PEM electrolysers within wind farms to produce green hydrogen, using optimisation models and real options analysis

Reducing electricity consumption peaks with parametrised dynamic pricing strategies given maximal unit prices

Demand response is a crucial mechanism for flattening of peak loads. For its implementation, we not only require consumers who react to price changes, but also intelligent strategies to select prices. We propose a parametrised meta-strategy for dynamic pricing and identify suitable strategies for given scenarios through offline optimisation using a population model. We also model an important and novel constraint: a price cap (a maximal unit price) for consumer protection. We show in computational simulations that the maximal unit price influences the peak reduction potential of dynamic pricing. We compare our dynamic pricing approach with a constant pricing approach and show that our approach, used by a profit-optimising seller, is both peak-reducing and equally profitable

Centralised battery flexibility assessment for imbalance management in Spain considering lithium-ion degradation mechanism

The rise of Distributed Energy Resources (DER) cause more uncertainty for the Balance Responsible Party (BRP) with regards to precise forecasting of supply and demand. In case of an imbalance, ancil- lary services have to be activated resulting in monetary deviation penalties for the respective BRP. The implementation of reliable communication technology enables the use of aggregated flexibility, utilising smart industrial and residential applications, heating systems, electric vehicles, distributed generation and energy storage. This work analyses the use case of centralised Lithium-Ion battery providing flexibility for BRPs. The analysis was conducted using Spanish electricity market data and real imbalance data from the year 2017. The model developed in this work integrates battery cycle aging using a piecewise linear cost function. This approach provides a close approximation of the battery degradation mechanism of electrochemical batteries and can be incorporated easily into existing market dispatch programs with short time window. Having battery degradation implemented in the decision making reveals a more considerate operation of the battery, which prolongs the battery’s lifetime and improves the project’s profitability. Moreover, the analysis revealed that the Lithium-Ion Titanate (LTO) chemistry performs the best from an operational and project-based perspective. Eventually, the business case of usin ng centralised battery flexibility for deviation management using the example of Spain was assessed and concluded to be insufficient to reach break-even for the investmen