The Creation, Validation, and Application of Synthetic Power Grids

Public test cases representing large electric power systems at a high level of fidelity and quality are few to non-existent, despite the potential value such cases would have to the power systems research community. Legitimate concern for the security of large, high-voltage power grids has led to tight restrictions on accessing actual critical infrastructure data. To encourage and support innovation, synthetic electric grids are fictional, designed systems that mimic the complexity of actual electric grids but contain no confidential information. Synthetic grid design is driven by the requirement to match wide variety of metrics derived from statistics of actual grids. The creation approach presented here is a four-stage process which mimics actual power system planning. First, substations are geo-located and internally configured from seed public data on generators and population. The substation placement uses a modified hierarchical clustering to match a realistic distribution of load and generation substations, and the same technique is also used to assign nominal voltage levels to the substations. With buses and transformers built, the next stage constructs a network of transmission lines at each nominal voltage level to connect the synthetic substations with a transmission grid. The transmission planning stage uses a heuristic inspired by simulated annealing to balance the objectives associated with both geographic constraints and contingency reliability, using a linearized dc power flow sensitivity. In order to scale these systems to tens of thousands of buses, robust reactive power planning is needed as a third stage, accounting for power flow convergence issues. The iterative algorithm presented here supplements a synthetic transmission network that has been validated by a dc power flow with a realistic set of voltage control devices to meet a specified voltage profile, even with the constraints of difficult power flow convergence for large systems. Validation of the created synthetic grids is crucial to establishing their legitimacy for engineering research. The statistical analysis presented in this dissertation is based on actual grid data obtained from the three major North American interconnects. Metrics are defined and examined for system proportions and structure, element parameters, and complex network graph theory properties. Several example synthetic grids are shown as examples in this dissertation, up to 100,000 buses. These datasets are available online. The final part of this dissertation discusses these specific grid examples and extensions associated with synthetic grids, in applying them to geomagnetic disturbances, visualization, and engineering education

Advances in Energy System Optimization

The papers presented in this open access book address diverse challenges in decarbonizing energy systems, ranging from operational to investment planning problems, from market economics to technical and environmental considerations, from distribution grids to transmission grids, and from theoretical considerations to data provision concerns and applied case studies. While most papers have a clear methodological focus, they address policy-relevant questions at the same time. The target audience therefore includes academics and experts in industry as well as policy makers, who are interested in state-of-the-art quantitative modelling of policy relevant problems in energy systems. The 2nd International Symposium on Energy System Optimization (ISESO 2018) was held at the Karlsruhe Institute of Technology (KIT) under the symposium theme “Bridging the Gap Between Mathematical Modelling and Policy Support” on October 10th and 11th 2018. ISESO 2018 was organized by the KIT, the Heidelberg Institute for Theoretical Studies (HITS), the Heidelberg University, the German Aerospace Center and the University of Stuttgart

Energy and Reserve Management in Interconnected Systems including Electric Railway and Public Power Grids:Operation, Market Strategies and Capacity Expansion

For historical reasons, the frequency of Electric Railway Power System (ERPS) in central European countries (e.g., Switzerland, Germany and Austria) is different from the frequency of the public power grid. To feed such a low frequency (i.e., 16.7 Hz) system, Electric Railway Companies (ERCs) operate their own low frequency generators. Moreover, ERPSs are connected to the public power grids through both static and rotating converters. The power and energy demand at ERPS substations is highly fluctuating, due to the movement of trains (mobile demands). Hence, ERC has to provide enough reserve services either from its generators or from interconnecting converters in order to ensure a secure and reliable operation of the system. The interconnection presents great advantages for both of the power grids. The most important benefits are reliability enhancement, sharing reserve service resources and energy trading opportunities due to the temporal diversity of the peak demand. Within the above context, the following three main problems referring to the operation of ERPS, strategies for participating in electricity markets and capacity expansion of interconnecting converters have been studied in this thesis. First, the problem of joint energy and reserve scheduling in an ERPS has been addressed. This problem has been formulated as a two-stage optimization problem including first (day-ahead scheduling) and second (real-time balancing) stages. In this problem, the variation of energy demand at each substation from its forecast value is an important uncertain parameter. To investigate the characteristics of this uncertain parameter, a short term load forecast method based on time series has been applied using realistic data from Swiss ERPS. Next, two mathematical approaches, namely, adaptive robust optimization and stochastic optimization have been proposed for dealing with uncertainties in this scheduling problem. The numerical results show that ERC can effectively utilize its generators and interconnecting converters to: 1) ensure security of its supply and 2) reduce its energy and reserve provision cost. Second, we propose a robust offering strategy method for ERC to participate in energy and reserve markets, in the sense that uncertainties in energy demand at ERPS substations would not threaten its security of supply. In this respect, a discrete robust optimization technique is used to solve the robust energy and reserve scheduling problem. Afterward, a reserve offering curve construction algorithm based on the solution of robust energy and reserve scheduling is proposed. This algorithmtakes into account the correlation between upward and downward tertiary reserve prices. To show the effectiveness of the proposed method, a realistic case study based on the characteristic of Swiss ERPS has been presented. Third, we propose and investigate methods to assess the capacity expansion of the interconnecting converters. In this respect, the effect of increasing the capacity of the interconnecting converters on the daily operation cost of ERC has been studied. Afterward, the effect of adding new interconnecting converters on the short circuit ratio of ERPS substations has been investigated. Finally, a cost-reliability approach has been proposed to find the optimal size and location of new interconnecting converters. This method has allowed us to provide ERC with a set of optimal solutions according to the different economic and technical criteria

Cross-border congestion management in the electricity market

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Flexitranstore

This open access book comprises 10 high-level papers on research and innovation within the Flexitranstore Project that were presented at the FLEXITRANSTORE special session organized as part of the 21st International Symposium on High Voltage Engineering. FLEXITRANSTORE (An Integrated Platform for Increased FLEXIbility in smart TRANSmission grids with STORage Entities and large penetration of Renewable Energy Sources) aims to contribute to the development of a pan-European transmission network with high flexibility and high interconnection levels. This will facilitate the transformation of the current energy production mix by hosting an increasing share of renewable energy sources. Novel smart grid technologies, control and storage methods, and new market approaches will be developed, installed, demonstrated, and tested introducing flexibility to the European power system. FLEXITRANSTORE is developing a next-generation Flexible Energy Grid (FEG) that will be integrated into the European Internal Energy Market (IEM) through the valorization of flexibility services. This FEG addresses the capabilities of a power system to maintain continuous service in the face of rapid and large swings in supply or demand. As such, a wholesale market infrastructure and new business models within this integrated FEG must be upgraded for network players, and offer incentives for new ones to join, while at the same time demonstrating new business perspectives for cross-border resource management and energy trading

Multicriteria methodologies for the appraisal of smart grid projects when flexibility competes with grid expansion

The severe consequences expected due to the increased frequency and intensity of extreme weather events call for improving the environmental sustainability of our society. The electricity sector is pivotal in the path toward a climate-neutral society. Nowadays, the massive use of renewable energy sources requires that electricity demand follows energy production. Demand has to be flexible, as well as the renewable generation and the grid infrastructures. The power system has to assume a decentralised structure and integrate the transportation and cooling and heating sectors. All customers connected to the electrical grid have to contribute to the power system management and participate in the related markets. The power system has to become smart; all technical and market processes have to be digitalised to enable new functionalities and services. The power system transformation requires rethinking planning and operation practices to accommodate the changes and take advantage of the related opportunities. The novel features and services available in the active and flexible power system will influence the customers' daily habits; therefore, the impacts generated by planning initiatives will cross the power system borders by impacting society as a whole. Since the power system will be operated closer to its technical limits, it is crucial to enhance the management of uncertainties by the increased accuracy of load and generation forecast. This thesis addresses the ongoing power system transformation by focusing on the distribution system, which will face unprecedented changes. This thesis concerns novel approaches for appraising the project initiatives based on the use of the users' flexibility connected to the grid. Traditional appraisal tools are no longer effective; therefore, decision-makers have to be supported with tools capable of capturing the complexity of the future power system in which flexibility measures compete with grid expansion. In this thesis, an assessment framework for smart grid initiatives which combines the cost-benefit analysis and the multi-criteria analysis proposed. Based on international guidelines, this framework allows for a systematic and simultaneous assessment of tangible and the intangible impacts considering conflicting criteria. To complete the assessment framework, a novel methodology which combines Regret Theory and multi-criteria analysis is proposed. The proposed methodology represents one of the main contributions of this dissertation. It supports the decision-maker to identify the most valuable option by decomposing the complex decision-making problem of smart grid planning and rejecting personal biases by avoiding the need for defining the evaluation criteria relevance. However, the stakeholders’ perspective can be included in terms of constraints for the minimax optimisation problem. In conclusion, the contribution of the thesis is to provide decision-making support tools for strategical power system planning. The research activities described in this document have been aimed at supporting system operators and regulatory bodies by providing tools for smart grid project appraisal and improving the accuracy of power system studies considering the novel context features

The Creation, Validation, and Application of Synthetic Power Grids

Public test cases representing large electric power systems at a high level of fidelity and quality are few to non-existent, despite the potential value such cases would have to the power systems research community. Legitimate concern for the security of large, high-voltage power grids has led to tight restrictions on accessing actual critical infrastructure data. To encourage and support innovation, synthetic electric grids are fictional, designed systems that mimic the complexity of actual electric grids but contain no confidential information. Synthetic grid design is driven by the requirement to match wide variety of metrics derived from statistics of actual grids. The creation approach presented here is a four-stage process which mimics actual power system planning. First, substations are geo-located and internally configured from seed public data on generators and population. The substation placement uses a modified hierarchical clustering to match a realistic distribution of load and generation substations, and the same technique is also used to assign nominal voltage levels to the substations. With buses and transformers built, the next stage constructs a network of transmission lines at each nominal voltage level to connect the synthetic substations with a transmission grid. The transmission planning stage uses a heuristic inspired by simulated annealing to balance the objectives associated with both geographic constraints and contingency reliability, using a linearized dc power flow sensitivity. In order to scale these systems to tens of thousands of buses, robust reactive power planning is needed as a third stage, accounting for power flow convergence issues. The iterative algorithm presented here supplements a synthetic transmission network that has been validated by a dc power flow with a realistic set of voltage control devices to meet a specified voltage profile, even with the constraints of difficult power flow convergence for large systems. Validation of the created synthetic grids is crucial to establishing their legitimacy for engineering research. The statistical analysis presented in this dissertation is based on actual grid data obtained from the three major North American interconnects. Metrics are defined and examined for system proportions and structure, element parameters, and complex network graph theory properties. Several example synthetic grids are shown as examples in this dissertation, up to 100,000 buses. These datasets are available online. The final part of this dissertation discusses these specific grid examples and extensions associated with synthetic grids, in applying them to geomagnetic disturbances, visualization, and engineering education

Production planning of energy systems: Cost and risk assessment for district heating

This dissertation is a collection of research articles that assess economic andoperational risk in production planning of district heating. District heatingsystems are typically coupled to the electricity system through cogenerationand power-to-heat technologies, and production planners must account foruncertainty stemming from changing weather, demands and prices. Years ofhigh-resolution data from the district heating system in Aarhus, Denmark havebeen used throughout the project to model the system and estimate uncertainties.Risk management tools have been developed to aid district heating operatorsand investment decision makers in short-, medium- and long-term productionplanning.Short-term production planning involves commitment of production unitsand trading on the electricity markets and relies on forecasts of the heat load.Weather predictions are a significant source of uncertainty for heat load forecasts,because the heat load is highly weather-dependent. I introduce the method ofensemble weather predictions from meteorology to heat load forecasting andcreate a probabilistic load forecast to estimate the weather-based uncertainty.Better estimates of the weather-based uncertainty can be applied to optimizesupply temperature control and reduce heat losses without compromising securityof supply in heat distribution systems.Consumer behavior is another substantial, but difficult to capture, source ofuncertainty in short-term heat load forecasts. I include local holiday data instate-of-the-art load forecasts to improve accuracy and capture how load patternschange depending on the behavior of the consumers. A small overall improvementin forecast accuracy is observed. The improvement is more significant on holidaysand special occasions that are difficult to forecast accurately.In medium-term production planning, there can be substantial economicpotential in performing summer shutdown of certain production units. Theshutdown decision carries significant risk, due to changing seasonal weatherpatterns. Based on 38 years of weather data, the uncertainty on the timing ofthe optimal decision is estimated. This information is used to develop practicaldecision rules that are robust to rare weather events and capable of realizingmore than 90% of the potential savings from summer shutdown.Long-term production planning decisions regarding investments in futuredistrict heating production systems are affected by uncertainty from changingelectricity prices, fuel prices and investment cost for technology. The effects ofthese uncertainties on a cost-optimal heat production system are explored, usingwell-established production and storage technologies and extensive multivariatesensitivity analysis. The optimal technology choices are highly stable and,taxes aside, large heat pumps and heat storages dominate the cost-optimal heatproduction systems. However, the uncertainty on the exact capacity allocationis substantial. Excluding heat production based on fossil fuels increases theuncertainty on the system cost, but drastically reduces the uncertainty on theoptimal capacity allocation

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters