Maximising the benefit of distributed wind generation through intertemporal Active Network Management

The role of distribution networks is changing. There is a significant drive, influenced by climate change and security of supply issues, to move electricity generation towards renewable technologies. This is leading to an increase in demand for renewable generation connections at the distribution network level and putting pressure on distribution network operators to change the 'fit-and-forget' philosophy of network operation to include more active approaches. In the UK this is seen through the development of Active Network Management schemes which manage distributed generation in real-time, applying constraints when required to maintain network limits. In parallel, technologies have been developed that are capable of providing intertemporal flexibility, of which two particular examples are energy storage and flexible demand. The objective of the thesis is to answer the questions: How can energy storage and flexible demand be scheduled in a second-generation Active Network Management scheme? And how should they be operated to gain most benefit from distributed wind generation? To answer these questions, the thesis develops and uses tools to study the optimisation of second-generation Active Network Management schemes including intertemporal technologies. The tools developed include a Dynamic Optimal Power Flow algorithm for management of energy storage and flexible demand. The thesis provides the first fully flexible model of energy storage in this context, the first implementation of principles-of-access in an optimal power flow, and the first detailed study of the role of energy storage and flexible demand in managing thermal limits and reducing curtailment of distributed wind generation. The thesis also develops the theory of Dynamic Locational Marginal Pricing based on the economic information contained in an optimal solution to a Dynamic Optimal Power Flow. The thesis shows this to be a useful way of understanding the economic impact of intertemporal flexibility and monetary flows in markets which contain them. The thesis goes on to provide a detailed report of the application of Dynamic Optimal Power Flow and Dynamic Locational Marginal Pricing to an islanded Active Network Management scheme currently in deployment in the UK. This highlights the ability of the tools developed to contribute to future projects. A conclusions of the thesis is that DOPF provides a useful method of scheduling flexible devices such as energy storage and power systems. It takes full account of network constraints and limitations, and as applied in this thesis, the most complete models of the intertemporal effects of energy storage and flexible demand to date. The studies contained in the thesis show that energy storage and flexible demand can increase the benefit of distributed wind generation in Active Network Management by minimising curtailment and transferring generated electricity to periods during which the energy has greatest value in offsetting expensive, fossil fuel based generation. The thesis notes the importance of a useful definition of the 'benefit' of wind generation in terms of global objectives such as minimising emissions rather than interim objectives such as maximising generation from renewables. The thesis discusses the importance of losses in energy storage, and the relationship of storage and network losses with curtailment of wind and the lost opportunity of generating electricity. In terms of losses, the extension of existing economic analysis methods leads to the result that flexibility will only operate between time-steps where the ratio of prices is greater than the round-trip losses of the store. Within this constraint, effective use of energy storage is shown to result from regular charging and discharging. The comparison between energy storage and flexible demand shows that where there are few losses associated with flexibility in demand it is significantly more successful than energy storage at mitigating the effects of variability in wind. The final study of an islanded distribution network with wind curtailment, concludes that energy storage is less effective that flexible demand at reducing wind curtailment, but can provide benefit through management of peak demand. Flexible demand, in the form of flexible domestic electric heating, is shown to have the ability to provide a significant benefit in terms of reduced wind curtailment. This ability is further enhanced for island situations if demand has a frequency-responsive component.The role of distribution networks is changing. There is a significant drive, influenced by climate change and security of supply issues, to move electricity generation towards renewable technologies. This is leading to an increase in demand for renewable generation connections at the distribution network level and putting pressure on distribution network operators to change the 'fit-and-forget' philosophy of network operation to include more active approaches. In the UK this is seen through the development of Active Network Management schemes which manage distributed generation in real-time, applying constraints when required to maintain network limits. In parallel, technologies have been developed that are capable of providing intertemporal flexibility, of which two particular examples are energy storage and flexible demand. The objective of the thesis is to answer the questions: How can energy storage and flexible demand be scheduled in a second-generation Active Network Management scheme? And how should they be operated to gain most benefit from distributed wind generation? To answer these questions, the thesis develops and uses tools to study the optimisation of second-generation Active Network Management schemes including intertemporal technologies. The tools developed include a Dynamic Optimal Power Flow algorithm for management of energy storage and flexible demand. The thesis provides the first fully flexible model of energy storage in this context, the first implementation of principles-of-access in an optimal power flow, and the first detailed study of the role of energy storage and flexible demand in managing thermal limits and reducing curtailment of distributed wind generation. The thesis also develops the theory of Dynamic Locational Marginal Pricing based on the economic information contained in an optimal solution to a Dynamic Optimal Power Flow. The thesis shows this to be a useful way of understanding the economic impact of intertemporal flexibility and monetary flows in markets which contain them. The thesis goes on to provide a detailed report of the application of Dynamic Optimal Power Flow and Dynamic Locational Marginal Pricing to an islanded Active Network Management scheme currently in deployment in the UK. This highlights the ability of the tools developed to contribute to future projects. A conclusions of the thesis is that DOPF provides a useful method of scheduling flexible devices such as energy storage and power systems. It takes full account of network constraints and limitations, and as applied in this thesis, the most complete models of the intertemporal effects of energy storage and flexible demand to date. The studies contained in the thesis show that energy storage and flexible demand can increase the benefit of distributed wind generation in Active Network Management by minimising curtailment and transferring generated electricity to periods during which the energy has greatest value in offsetting expensive, fossil fuel based generation. The thesis notes the importance of a useful definition of the 'benefit' of wind generation in terms of global objectives such as minimising emissions rather than interim objectives such as maximising generation from renewables. The thesis discusses the importance of losses in energy storage, and the relationship of storage and network losses with curtailment of wind and the lost opportunity of generating electricity. In terms of losses, the extension of existing economic analysis methods leads to the result that flexibility will only operate between time-steps where the ratio of prices is greater than the round-trip losses of the store. Within this constraint, effective use of energy storage is shown to result from regular charging and discharging. The comparison between energy storage and flexible demand shows that where there are few losses associated with flexibility in demand it is significantly more successful than energy storage at mitigating the effects of variability in wind. The final study of an islanded distribution network with wind curtailment, concludes that energy storage is less effective that flexible demand at reducing wind curtailment, but can provide benefit through management of peak demand. Flexible demand, in the form of flexible domestic electric heating, is shown to have the ability to provide a significant benefit in terms of reduced wind curtailment. This ability is further enhanced for island situations if demand has a frequency-responsive component

Detailed long-term hydro-thermal scheduling for expansion planning in the Nordic power system

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Reliability standards for the operation and planning of future electricity networks

Electricity networks, designed and operated in accordance with the historic deterministic standards, have broadly delivered secure and reliable supplies to customers. A key issue regarding their evolution is how the operation and planning standards should evolve to make efficient use of the existing assets while taking advantage of emerging, non-network (or non-wires) technologies. Deployment of the smart grid will require fundamental changes in the historical principles used for network security in order to ensure that integration of low-carbon generation is undertaken as efficiently as possible through the use of new information and communication technology (ICT), and new flexible network technologies that can maximize utilization of existing electricity infrastructure. These new technologies could reduce network redundancy in providing security of supply by enabling the application of a range of advanced, technically effective, and economically efficient corrective (or post-fault) actions that can release latent network capacity of the existing system. In this context, this paper demonstrates that historical deterministic practices and standards, mostly developed in the 1950s, should be reviewed in order to take full advantage of new emerging technologies and facilitate transition to a smart grid paradigm. This paper also demonstrates that a probabilistic approach to developing future efficient operating and design strategies enabled by new technologies, will appropriately balance network investment against non-network solutions while truly recognizing effects of adverse weather, common-mode failures, high-impact low-probability events, changing market prices for pre- and post-contingency actions, equipment malfunctioning, etc. This clearly requires explicit consideration of the likelihood of various outages (beyond those considered in deterministic studies) and quantification of their impacts on alternative network operation and investment decisions, which cannot be undertaken in a deterministic, “one size fits all” framework. In this context, we developed advanced optimization models aimed at determining operational and design network decisions based on both deterministic and probabilistic security principles. The proposed models can recognize network constraints/congestion and various operational measures (enabled by new technologies) composed of preventive and corrective control actions such as operation of special protection schemes, demand side response and generation reserve utilization and commitment, considering potential outages of network and generation facilities. The probabilistic model proposed can also provide targeted levels of reliability and limit exposure to severe low probability events (mainly driven by natural hazards) through the use of Conditional Value at Risk (CVaR) constraints, delivering robust and resilient supplies to consumers at the minimum cost. Through various case studies conducted on the Great Britain (GB) power network, we set out the key questions that need to be addressed in support of the change in network reliability paradigm, provide an overview of the key modelling approaches proposed for assessing the risk profile of operation of future networks, propose a framework for a fundamental review of the existing network security standards, and set out challenges for assessing the reliability and economics of the operation of future electricity network

Self-consumption through power-to-heat and storage for enhanced PV integration in decentralised energy systems

Many countries have adopted schemes to promote investments into renewable energy sources resulting, among others, in a high penetration of solar PV energy. The system integration of the increasing amount of variable electricity generation is therefore a highly important task. This paper focuses on a residential quarter with PV systems and explores how heat pumps and thermal and electrical storages can help to integrate the PV generation through self-consumption. However, self-consumption and PV integration are not only affected by technologies but also by pricing mechanisms. This paper therefore analyses the impact of different tariffs on the investment and operation decisions in a residential quarter and its interaction with the external grid. The considered tariffs include a standard fixed per-kilowatt-hour price, a dynamic pricing scheme and a capacity pricing scheme. To account for the interdependent uncertainties of energy supply, demand and electricity prices, we use a module-based framework including a Markov process and a two-stage stochastic mixed-integer program. Analysing a residential quarter in Southern Germany as a case study, we find that the integration of a PV system is economically advantageous for all considered tariffs. The self-consumption rate varies between 58 and 75%. The largest PV system is built when dynamic prices are applied. However, the peak load from the external grid increases by a factor of two under this tariff without any incentive for reduction. In contrast, capacity pricing results in a reduction of the peak load by up to 35%

Scenario modelling and optimisation of renewable energy integration for the energy transition

A large number of countries have engaged themselves in an energy transition towards more re- newable energy in their energy systems. Motivations stem mainly from the need to reduce CO2 emissions, and from a desire of their population to phase out technologies such as nuclear. Most of these countries promote biomass, wind and solar energy sources, among other possibilities. How- ever the current rate of deployment of renewable energy systems globally is not sufficient to reach the CO2 emissions reduction that would allow to maintain the global average temperature increase below the 2°C threshold. The main barriers to a wider integration of renewable energy systems are i) their limited realisable potential, ii) their still limited competitiveness, iii) their intermittence; iv) public acceptance often related to poor level of energy literacy amongst citizens. Citizens are key decision-makers. They must decide on energy policies and on the energy technologies they use, hence they have the power to foster or halt the energy transition. This thesis presents two different strategies for addressing the problem of the integration of re- newable energy sources for energy transitions. The first one (Chapter 1) consists in developing an energy modelling tool to help decision-makers understand the energy system and find their own answers. The modelling approach also includes a new methodology for the calculation of the total cost of a national energy system. A model of the Swiss energy system has been created following this approach, which serves as basis to develop the Swiss-Energyscope online calculator. This calculator and its model present an optimal trade-off between scientific rigour and user-friendliness, which allows the reproduction of the energy transition scenarios conceived by the Swiss Government, and consequently its use for energy policy making. The second strategy (chapters 2 and 3) profits from the possibilities offered by mathematical mod- elling and optimisation to analyse national energy systems, and derive insights for policy and decision-makers. First, a methodology using a mix-integer linear programming (MILP) model analyses biomass usage pathways to determine its optimal use in Switzeland in 2035. Second, in order to study the role of biomass, non-linear optimisation is applied to create future scenarios. (Chapter 3) focuses on the solutions to deal with the variability of renewable electricity. To this end, a MILP model with hourly time resolution is conceived to study the use of flexible electricity supply and demand options for the integration of renewable electricity. The optimisation methodologies are validated on case studies for the Swiss energy system. Regarding biomass, the results reveal that woody biomass chemical conversion technologies can allow for an overall better performance in terms of CO2 avoided emissions compared to direct combustion, as long as the produced biofuels are used in efficient technologies. Results also show that the combination of the gasification-methanation process of woody biomass with the production of H2 produced from excess electricity would allow to reduce the Swiss natural gas imports to zero by 2050. Concerning the integration of variable renewable electricity, the cost difference between using flexible electricity supply- and demand-options or electricity imports to deal with variable renewable electricity is below 2.5% of the total cost of the energy system

Modelling the future development of renewable energy technologies in the European electricity sector using agent-based simulation

Increasing the share of renewable energy sources in final energy consumption forms an important part of the EU\u27s energy and climate strategy. An agent-based simulation model is developed to assess future diffusion processes of renewable energy technologies under different policy regimes. The developed model helps to design support policies, or point out existing investment opportunities for interested stakeholders

Energy system analysis of energy autonomous municipalities

Attention on decentralised autonomous energy systems has increased exponentially in the past three decades, as demonstrated by the absolute number of real-world projects and the share of publications in the corpus of scientific literature. This is due to the energy transition and the related environmental awareness as well as the desire of citizens to play an active role in energy supply and to be less dependent on central markets and structures. However, local decision-makers, who often lack the necessary expertise, need decision support in energy system planning. To this end, this thesis follows the objective to develop novel methods for the technical, economic and environmental assessment of a large number of completely energy autonomous municipalities and their impacts on the overall energy system. Completely energy autonomous municipalities are disconnected from the gas and electricity grid and supply themselves with energy from plants owned and operated by the municipality. Novel methods of energy system analysis were developed in this thesis as part of seven original research articles. Germany is used as a case study, but the general approach, methods and results are transferable to other contexts. First of all, the 11,131 German municipalities were clustered with regard to their suitability for decentralised energy systems. Based on this municipality typology, representative municipalities were selected to be investigated in an already existing holistic municipal energy system optimisation model (RE³ASON). This model was extended by novel and transferable approaches to design deep geothermal plants and district heating networks. These base-load capable technologies were selected to reduce the storage costs in energy autonomous municipalities. The technical feasibility and economic expenditures of energy autonomy could finally be determined in all 11,131 German municipalities by combining the extended energy system optimisation model with a stepwise linear regression. The energy system optimisations showed that in the case of complete energy autonomy, deep geothermal plants in combination with district heating networks could reduce the total costs by up to 50%. On average, the energy system costs until 2030 in German municipalities increase by about 0.41 €/kWh in the energy autonomous case compared to the optimised reference case with grid connection. While a technical potential to achieve energy autonomy is present in 56% of the German municipalities, there seem to be no economic advantages through energy autonomy compared to the optimised reference energy system. The novel methodological approach of this thesis enabled to obtain optimisation results for a high number of energy systems (6,314 municipalities) with practicable computational expenses. In addition to the original data and planning tools published alongside the articles, the findings of this thesis can also support local decision makers in determining suitable municipal energy systems. In order to increase the realizability of the case study results, some methodological extensions should be investigated in future studies such as other perspectives than that of a central planner, higher temporal model resolutions or social aspects like consumer acceptance of specific technologies or a security of supply below 100%

Optimisation of systems with storage with application to to time-varying electricity tariffs

Systems with storage allow the production and use of a commodity to be separated in time to reduce costs or to make better use of available capacity. Hydro-reservoirs play a central role in many electricity systems. On the demand side there is a much greater variety of storage plant; buffer storages in manufacturing, ice storage systems and compressed air systems. Battery storage can also be used in remote area power supply systems (RAPS). Determining an effective and efficient operating strategy for storages can be difficult. The literature reveals a wide variety of approaches to the hydro-dispatch problem. More recently more emphasis has been placed on the operation of distributed demand-side storages, be they centrally controlled or individually influenced through time-of-use or spot pricing tariffs. The difficulty of modelling and optimising the operation of storage systems arises from the separation over time of production and use of the stored commodity. Determining the optimal operating strategy is a time-staged problem, presenting practical difficulties with problem size. The operating strategy also depends on expectations of future plant operation and external conditions which cannot always be known with certainty. This thesis presents an exact and efficient solution method for a general class of deterministic, single storage systems. While many real systems are more complex than this, the approach developed combines elements of both dynamic programming and general mathematical programming methodology and so offers good prospects for extension to more complex multiple storage or stochastic systems. An important insight used throughout this thesis is that, for a large class of storage problems, the "production" and "storage" elements of the system can be separated. This leads to the further insight that the behaviour of a wide variety of production systems can be encapsulated in a single "production cost function" which describes the way all the system costs per unit time vary with the rate of flow into (or out of) the store. For the purpose of this thesis, this function is taken to be piece-wise linear and convex, although such restrictions can largely be removed if the algorithm is modified. Once the production element of the system can be described in this standardised way, it is possible to write both linear programming and dynamic programming representations of the time-staged optimisation problem to be solved. By analysing the mathematical properties of this formulation and the conditions for its solution, a simple, exact and highly efficient solution algorithm is developed. One advantage of the algorithm is that it has a simple and intuitive graphical representation. The algorithm combines the best features of the linear and dynamic programming approaches while eliminating their worst features for the class of problem addressed. As a dynamic programming approach, the solution is obtained by solving a sequence of small, single period optimisations, which is much more efficient than solving a time-stage linear program. As a linear programming approach, the solution is exact and obtained without discretising the storage variable. The dual properties of the linear programming solution also provide useful supplementary information such as the shadow value of the storage contents over time. As a practical matter, commercial codes for the storage algorithm can be developed by extending existing mathematical programming codes. Two examples are presented. The first works through a simple model analytically to illustrate the workings of the algorithm. The second is a larger and more complex model of a pumped storage hydro-electric system. While the thesis concentrates on single storage, deterministic systems, possible extensions to deal with multiple storage and stochastic systems are also reviewed

Risk-Averse Model Predictive Operation Control of Islanded Microgrids

In this paper we present a risk-averse model predictive control (MPC) scheme for the operation of islanded microgrids with very high share of renewable energy sources. The proposed scheme mitigates the effect of errors in the determination of the probability distribution of renewable infeed and load. This allows to use less complex and less accurate forecasting methods and to formulate low-dimensional scenario-based optimisation problems which are suitable for control applications. Additionally, the designer may trade performance for safety by interpolating between the conventional stochastic and worst-case MPC formulations. The presented risk-averse MPC problem is formulated as a mixed-integer quadratically-constrained quadratic problem and its favourable characteristics are demonstrated in a case study. This includes a sensitivity analysis that illustrates the robustness to load and renewable power prediction errors