349 results found
Gong X, De Paola A, Angeli D, et al., 2019, A game-theoretic approach for price-based coordination of flexible devices operating in integrated energy-reserve markets, Energy, Vol: 189, Pages: 1-12, ISSN: 0360-5442
This paper presents a novel distributed control strategy for large scale deployment of demand response. In the considered framework, large populations of storage devices and electric vehicles (EVs) participate to an integrated energy-reserve market. They react to prices and autonomously schedule their operation in order to optimize their own objective functions. The price signals are obtained through the resolution of an optimal power flow problem that explicitly takes into account the impact of demand response on the optimal power dispatch and reserve procurement of committed generation. Differently from previous approaches, the adopted game-theoretic framework provides rigorous theoretical guarantees of convergence and optimality of the proposed control scheme in a multi-price setup that includes ancillary services. The performance of the coordination scheme is also evaluated in simulation on the PJM 5-bus system, demonstrating its capability to flatten demand profiles and reduce the costs of generators and flexible devices.
Orths A, Anderson CL, Brown T, et al., 2019, Flexibility from energy systems integration: Supporting synergies among sectors, IEEE Power and Energy Magazine, Vol: 17, Pages: 67-78, ISSN: 1540-7977
© 2003-2012 IEEE. Energy systems integration, or sector coupling, has several drivers that span climate impact mitigation and economics to social and regulatory considerations. A key question is what is sector coupling, and how does it impact the flexibility of the energy system? Here, the energy system includes several sectors—electricity, gas, heat, and transportation—that have been independent for decades in most countries except for their coupling via combined heat and power (CHP) units. In energy systems integration, some sectors may provide flexibility to other sectors, while other sectors will require flexibility when interlinking. To support these synergies among sectors, it is important to explore and quantify mutual interactions as well as seek examples of how these integrations can provide flexibility and other benefits. From the perspective of the electricity sector, it is important to ensure that there is enough flexibility in the interconnected systems to support decarbonization goals, such as those set in the Paris Agreement, while ensuring operational reliability.
De Paola A, Trovato V, Angeli D, et al., 2019, A mean field game approach for distributed control of thermostatic loads acting in simultaneous energy-frequency response markets, IEEE Transactions on Smart Grid, Vol: 10, Pages: 5987-5999, ISSN: 1949-3053
This paper proposes a novel distributed solution for the operation of large populations of thermostatically controlled loads (TCLs) providing frequency support. A game-theory framework is adopted, modeling the TCLs as price-responsive rational agents that schedule their energy consumption and allocate frequency response provision in order to minimize their operational costs. The novelty of this work lies in the use of mean field games to abstract the complex interactions of large numbers of TCLs with the grid and in the introduction of an innovative market structure, envisioning distinct price signals for electricity and response. Differently from previous approaches, such prices are not designed ad hoc but are derived instead from an underlying system scheduling model.
Badesa L, Teng F, Strbac G, 2019, Simultaneous scheduling of multiple frequency services in stochastic unit commitment, IEEE Transactions on Power Systems, Vol: 34, Pages: 3858-3868, ISSN: 0885-8950
The reduced level of system inertia in low-carbon power grids increases the need for alternative frequency services. However, simultaneously optimising the provision of these services in the scheduling process, subject to significant uncertainty, is a complex task given the challenge of linking the steady-state optimisation with frequency dynamics. This paper proposes a novel frequency-constrained Stochastic Unit Commitment (SUC) model which, for the first time, co-optimises energy production along with the provision of synchronised and synthetic inertia, Enhanced Frequency Response (EFR), Primary Frequency Response (PFR) and a dynamically-reduced largest power infeed. The contribution of load damping is modelled through a linear inner approximation. The effectiveness of the proposed model is demonstrated through several case studies for Great Britain’s 2030 power system, which highlight the synergies and conflicts among alternative frequency services, as well as the significant economic savings and carbon reduction achieved by simultaneously optimising all these services.
Gong X, De Paola A, Angeli D, et al., 2019, Distributed coordination of flexible loads using locational marginal prices, IEEE Transactions on Control of Network Systems, Vol: 6, Pages: 1097-1110, ISSN: 2325-5870
This paper presents a novel distributed control strategy for large-scale deployment of flexible demand in power systems. A game theoretical setting is adopted, modeling the loads as rational players that aim to complete an assigned task at minimum cost and compete for power consumption at the cheapest hours of the day. The main novelty is the analysis of power systems with congestion: the proposed modeling framework envisages heterogeneous groups of loads that operate at different buses, connected by transmission lines of limited capacity. The locational marginal prices of electricity, different in general for each bus, are calculated through an optimal power flow problem, accounting for the impact of the flexible devices on power demand and generation. A new iterative scheme for flexible demand coordination is analytically characterized as a multivalued mapping. Its convergence to a stable market configuration (i.e., variational Wardrop equilibrium) and global optimality are analytically demonstrated, for any penetration level of flexible demand and any grid topology. Distributed implementations of the proposed control strategy are discussed, evaluating their performance with simulations on the IEEE 24-bus system.
Sun M, Konstantelos I, Strbac G, 2019, A deep learning-based feature extraction framework for system security assessment, IEEE Transactions on Smart Grid, Vol: 10, Pages: 5007-5020, ISSN: 1949-3061
The ongoing decarbonisation of modern electricity systems has led to a substantial increase of operational uncertainty, particularly due to the large-scale integration of renewable energy generation. However, the expanding space of possible operating points renders necessary the development of novel security assessment approaches. In this paper we focus on the use of security rules, where classifiers are trained offline to characterize previously unseen points as safe or unsafe. This paper proposes a novel deep learning-based feature extraction framework for building security rules. We show how deep autoencoders can be used to transform the space of conventional state variables (e.g. power flows) to a small number of dimensions where we can optimally distinguish between safe and unsafe operation. The proposed framework is data-driven and can be useful in multiple applications within the context of security assessment. To achieve high accuracy, a novel objective-based loss function is proposed to address the issue of imbalanced safe/unsafe classes that characterizes electricity system operation. Furthermore, an R-vine copula-based model is proposed to sample historical data and generate large populations of anticipated system states for training. The superior performance of the proposed framework is demonstrated through a series of case studies and comparisons using the load and wind generation data from the French transmission system, which have been mapped to the IEEE 118-bus system.
Wu Z, Guo F, Polak J, et al., 2019, Evaluating grid-interactive electric bus operation and demand response with load management tariff, Applied Energy, ISSN: 0306-2619
Electric Vehicles are expected to play a vital role in the transition of smart energy systems. Lots of recent research has explored numerous underlying mechanisms to achieve the synergetic interactions in the electricity balancing process. In this paper, the grid-interactive operation of electric buses is first time integrated within a dynamic market frame using the Distribution Locational Marginal Price algorithm for load congestion management. Since the defined problem correlates the opportunity charging flexibility with the bus mobility over a network, the tempo-spatial distribution of energy needs can be reflected in the dynamic of service planning. The interactions between bus operators and suppliers are quantitatively modelled by a bi-level optimisation process to represent the electric bus service planning and electricity market clearing separately. The effectiveness of the proposed load management has been demonstrated using data collected from an integrated real-world bus network. Experiments show that engagement of electric bus charging load in demand response is helpful to alleviate the network congestion and to reduce the power loss by 7.2% in the distribution network. However, alleviated charging loads have exhibited counter-intuitive ability for load shifting. The restricted electric bus operational requirements leads to a 8.17% loss of charging demand, while the reliance on large batteries has increased by 10.57%. However, the sensitivity analysis also shows that as the battery cost declines, the such discourage implications on grid-interactive electric bus operation will decrease once the battery cost below 190/kWh. The optimal grid-ebus integration have to consider the trade-off between range add-up, reduced battery cost and additional benefits.
Ye Y, Qiu D, Sun M, et al., Deep Reinforcement Learning for Strategic Bidding in Electricity Markets, IEEE Transactions on Smart Grid, ISSN: 1949-3061
Bi-level optimization and reinforcement learning (RL) constitute the state-of-the-art frameworks for modeling strategic bidding decisions in deregulated electricity markets. However, the former neglects the market participants' physical non-convex operating characteristics, while conventional RL methods require discretization of state and / or action spaces and thus suffer from the curse of dimensionality. This paper proposes a novel deep reinforcement learning (DRL) based methodology, combining a deep deterministic policy gradient (DDPG) method with a prioritized experience replay (PER) strategy. This approach sets up the problem in multi-dimensional continuous state and action spaces, enabling market participants to receive accurate feedback regarding the impact of their bidding decisions on the market clearing outcome, and devise more profitable bidding decisions by exploiting the entire action domain, also accounting for the effect of non-convex operating characteristics. Case studies demonstrate that the proposed methodology achieves a significantly higher profit than the alternative state-of-the-art methods, and exhibits a more favourable computational performance than benchmark RL methods due to the employment of the PER strategy.
Sun M, Strbac G, Djapic P, et al., 2019, Preheating quantification for smart hybrid heat pumps considering uncertainty, IEEE Transactions on Industrial Informatics, Vol: 15, Pages: 4753-4763, ISSN: 1551-3203
The deployment of smart hybrid heat pumps can introduce considerable benefits to electricity systems via smart switching between electricity and gas while minimizing the total heating cost for each individual customer. In particular, the fully-optimized control technology can provide flexible heat that redistributes the heat demand across time for improving the utilization of low-carbon generation and enhancing the overall energy efficiency of the heating system. To this end, accurate quantification of preheating is of great importance to characterize the flexible heat. This paper proposes a novel data-driven preheating quantification method to estimate the capability of heat pump demand shifting and isolate the effect of interventions. Varieties of fine-grained data from a real-world trial are exploited to estimate the baseline heat demand using Bayesian deep learning while jointly considering epistemic and aleatoric uncertainties. A comprehensive range of case studies are carried out to demonstrate the superior performance of the proposed quantification method and then, the estimated demand shift is used as an input into the whole-system model to investigate the system implications and quantify the range of benefits of rolling-out the smart hybrid heat pumps developed by PassivSystems to the future GB electricity systems.
Sun M, Djapic P, Aunedi M, et al., 2019, Benefits of smart control of hybrid heat pumps: an analysis of field trial data, Applied Energy, Vol: 247, Pages: 525-536, ISSN: 0306-2619
Smart hybrid heat pumps have the capability to perform smart switching between electricity and gas by employing a fully-optimized control technology with predictive demand-side management to automatically use the most cost-effective heating mode across time. This enables a mechanism for delivering flexible demand-side response in a domestic setting. This paper conducts a comprehensive analysis of the fine-grained data collected during the world’s first sizable field trial of smart hybrid heat pumps to present the benefits of the smart control technology. More specifically, a novel flexibility quantification framework is proposed to estimate the capability of heat pump demand shifting based on preheating. Within the proposed framework, accurate estimation of baseline heat demand during the days with interventions is fundamentally critical for understanding the effectiveness of smart control. Furthermore, diversity of heat pump demand is quantified across different numbers of households as an important input into electricity distribution network planning. Finally, the observed values of the Coefficient of Performance (COP) have been analyzed to demonstrate that the smart control can optimize the heat pump operation while taking into account a variety of parameters including the heat pump output water temperature, therefore delivering higher average COP values by maximizing the operating efficiency of the heat pump. Finally, the results of the whole-system assessment of smart hybrid heat pumps demonstrate that the system value of smart control is between 2.1 and 5.3 £ bn/year.
Sun M, Zhang T, Wang Y, et al., 2019, Using Bayesian deep learning to capture uncertainty for residential net load forecasting, IEEE Transactions on Power Systems, ISSN: 0885-8950
Decarbonization of electricity systems drives significant and continued investments in distributed energy sources to support the cost-effective transition to low-carbon energy systems. However, the rapid integration of distributed photovoltaic (PV) generation presents great challenges in obtaining reliable and secure grid operations because of its limited visibility and intermittent nature. Under this reality, net load forecasting is facing unprecedented difficulty in answering the following question: how can we accurately predict the net load while capturing the massive uncertainties arising from distributed PV generation and load, especially in the context of high PV penetration? This paper proposes a novel probabilistic day-ahead net load forecasting method to capture both epistemic uncertainty and aleatoric uncertainty using Bayesian deep learning, which is a new field that combines Bayesian probability theory and deep learning. The proposed methodological framework employs clustering in subprofiles and considers residential rooftop PV outputs as input features to enhance the performance of aggregated net load forecasting. Numerical experiments have been carried out based on fine-grained smart meter data from the Australian grid with separately recorded measurements of rooftop PV generation and loads. The results demonstrate the superior performance of the proposed scheme compared with a series of state-of-theart methods and indicate the importance and effectiveness of subprofile clustering and high PV visibility.
Oulis Rousis A, Konstantelos I, Strbac G, 2019, A planning model for a hybrid AC/DC microgrid using a novel GA/AC OPF algorithm, IEEE Transactions on Power Systems, ISSN: 0885-8950
This paper focuses on developing an appropriate combinatorial optimization technique for solving the optimal sizing problem of hybrid AC/DC microgrids. A novel two-stage iterative approach is proposed. In the first stage, a metaheuristic technique based on a tailor-made genetic algorithm is used to tackle the optimal sizing problem, while, in the second, a non-linear solver is deployed to solve the operational problem subject to the obtained design/investment decisions. The proposed approach, being able to capture technical characteristics such as voltage and frequency through a detailed power flow algorithm, provides accurate solutions and therefore can address operational challenges of microgrids. Its capability to additionally capture contingencies ensures that the proposed sizing solutions are suitable both during normal operation and transient states. Finally, the genetic algorithm provides convergence of the model with relative computational simplicity. The proposed model is applied to a generalizable microgrid comprising of AC and DC generators and loads, as well as various types of storage technologies in order to demonstrate the benefits. The load and natural resources data correspond to real data.
Takis-Defteraios G, Papadaskalopoulos D, Ye Y, et al., Role of Flexible Demand in Supporting Market-Based Integration of Renewable Generation, 13th IEEE PES PowerTech Conference, Publisher: IEEE
Previous work has analyzed the renewable generation hosting capacity of electricity systems and the relevant value of flexible demand from a techno-economic perspective, considering the balancing and network challenges associated with a large-scale integration of renewables. This paper investigates these issues from a market perspective, considering the challenges of low energy prices and renewables’ investment cost recovery. In this context, a multi-period bi-level optimization model is developed, where the upper level maximizes the renewable generation capacity subject to the long-term profitability constraint and the lower level represents the market clearing process, accounting for the time-coupling operational constraints of flexible demand. This bi-level problem is solved after converting it to a single-level mixed-integer linear problem (MILP). Case studies validate the proposed model and demonstrate that demand flexibility increases the maximum renewable generation capacity that can be integrated in the system without violating its profitability constraint.
Cremer J, Konstantelos I, Strbac G, From optimization-based machine learning to interpretable security rules for operation, IEEE Transactions on Power Systems, ISSN: 0885-8950
Various supervised machine learning approaches have been used in the past to assess the power system security (also known as reliability). This is typically done by training a classifier on a large number of operating points whose post-fault status (stable or unstable) has been determined via time-domain simulations. The output of this training process can be expressed as a security rule that is used online to classify an operating point. A critical, and little-studied aspect of these approaches is the interpretability of the rules produced. The lack of interpretability is a well-known issue of some machine learning approaches, especially when dealing with difficult classification problems. In the case of the security assessment of the power system, which is a complex mission-critical task, interpretability is a key requirement for the adoption and deployment by operators ofthese approaches.In this paper, for the first time, we explore the trade-offbetween predictive accuracy and interpretability in the contextof power system security assessment. We begin by demonstratinghow Decision Trees (DTs) can be used to learn data-driven security rules and use the tree depth as a measure for interpretability.We leverage disjunctive programming to formulate novel training methods, capable of learning high-quality DTs while stillmaintaining interpretability. In particular, we propose two newapproaches: (i) Optimal Classification Trees (OCT∗) is proposedfor training DTs of low-depth and (ii) Greedy Optimizationbased Tree (GOT) is proposed for training DTs of intermediate depth, where the increased computational burden is managed by exploiting the nested tree structure. We also demonstrate that the ability to generate high-quality interpretable rules can actually translate to impressive benefits in terms of training requirements. Through case studies on the IEEE 68-bus system, we demonstrate that the proposed methods can produce DTs of higher quality compared to the state-
Lee H, Byeon G-S, Jeon J-H, et al., 2019, An Energy Management System With Optimum Reserve Power Procurement Function for Microgrid Resilience Improvement, IEEE ACCESS, Vol: 7, Pages: 42577-42585, ISSN: 2169-3536
Oderinwale T, Ye Y, Papadaskalopoulos D, et al., Impact of energy storage on market-based generation investment planning, 13th IEEE PES PowerTech Conference Milano 2019, Publisher: IEEE
Previous work has analyzed the role of energy storage (ES) on generation investment planning through centralised cost-minimization models which are inherited from the era of regulated electricity utilities. This paper investigates this issue in the context of the deregulated market environment by proposing a new strategic generation investment planning model. The decision making of a strategic generation company is modeled through a multi-period bi-level optimization problem, where the upper level determines the profit-maximizinginvestment decisions of the generation company and the lower level represents themarket clearing process, accounting for the time-coupling operational characteristics of ES. This bi-level problem is solved after converting it to a single-level mixed-integer linear problem (MILP). Case studies demonstrate thatthe introduction of ES reduces the total generation capacity investment and enhances investments in “must-run” baseload generation over flexible peaking generation, yielding significant system cost savings.
Trovato V, Sanz IM, Chaudhuri B, et al., 2019, Preventing cascading tripping of distributed generators during non-islanding conditions using thermostatic loads, INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, Vol: 106, Pages: 183-191, ISSN: 0142-0615
Strbac G, Pudjianto D, Aunedi M, et al., 2019, Cost-effective decarbonization in a decentralized market the benefits of using flexible technologies and resources, IEEE Power and Energy Magazine, Vol: 17, Pages: 25-36, ISSN: 1540-7977
Qadrdan M, Fazeli R, Jenkins N, et al., 2019, Gas and electricity supply implications of decarbonising heat sector in GB, ENERGY, Vol: 169, Pages: 50-60, ISSN: 0360-5442
Oulis Rousis A, Konstantelos I, Fatouros P, et al., 2019, An AC OPF with voltage – frequency coupling constraints for addressing operational challenges of AC/DC microgrids, REM2018: Renewable Energy Integration with Mini/Microgrid, Publisher: Elsevier, Pages: 261-266, ISSN: 1876-6102
This paper focuses on developing an appropriate optimization technique for solving the optimal operation problem of hybrid AC/DC microgrids. A non-linear mathematical formulation is deployed to solve the problem subject to the exhaustive set of constraints and equations pertaining to the operation of AC and DC networks. The proposed approach, being able to capture technical characteristics, such as voltage and frequency, through the detailed power flow algorithm, provides accurate solutions and therefore can address operational challenges of MGs. The approach is applied to a highly-generalizable microgrid comprising of AC and DC generators and loads, as well as storage technologies in order to demonstrate the benefits. The simulation results demonstrate how voltage and frequency are effectively captured across the whole network via the utilised formulation and the power flow through the interlinking converter is associated with frequency (i.e. 49-51 Hz) and voltage variation (i.e. 0.95-1.05 p.u.).
Sun M, Wang Y, Strbac G, et al., 2019, Probabilistic peak load estimation in smart cities using smart meter data, IEEE Transactions on Industrial Electronics, Vol: 66, Pages: 1608-1618, ISSN: 0278-0046
Adequate capacity planning of substationsand feeders primarily depends on an accurate estimationof the future peak electricity demand. Traditional coinci-dent peak demand estimation is carried out based on theempirical metric, after diversity maximum demand (ADMD),indicating individual peak consumption levels and of de-mand diversification across multiple residents. With theprivilege of smart meters in smart cities, this paper pro-poses a data-driven probabilistic peak demand estima-tion framework using fine-grained smart meter data andsocio-demographic data of the consumers, which drivefundamental electricity consumptions across different cat-egories. In particular, four main stages are integrated inthe proposed approach: load modeling and sampling viathe proposed variable truncated R-vine copulas (VTRC)method; correlation-based customer grouping; probabilis-tic normalized maximum diversified demand (NMDD) esti-mation; and probabilistic peak demand estimation for newcustomers. Numerical experiments have been conductedon real demand measurements across 2,639 households inLondon, collected from Low Carbon London (LCL) projectssmart-metering trial. The mean absolute percentage error(MAPE) and pinball loss function are used to quantitativelydemonstrate the superiority of the proposed approach interms of the point estimate value and the probabilisticresult, respectively.
Sun M, Wang Y, Teng F, et al., 2019, Clustering-based residential baseline estimation: a probabilistic perspective, IEEE Transactions on Smart Grid, ISSN: 1949-3061
Demand Response (DR) is one of the most cost-effective solutions for providing flexibility to power systems. The extensive deployment of DR trials and the roll-out of smart meters enable the quantification of consumer responsiveness to price signals via baseline estimation. The traditional deterministic baseline estimation approach can provide only a single value without consideration of uncertainty. This paper proposes a novel probabilistic baseline estimation framework that consists of a daily load profile pool construction stage, a deep learning-based clustering stage, an optimal cluster selection stage, and a quantile regression forests model construction stage. In particular, the concept of a daily load profile pool is introduced, and a deep-learning-based clustering approach is employed to handle a large number of daily patterns to further improve the baseline estimation performance. Case studies have been conducted on fine-grained smart meter data collected from a real dynamic time-of-use (dTOU) tariffs trial of the Low Carbon London (LCL) project. The superior performance of the proposed method is demonstrated based on a series of evaluation metrics regarding both deterministic and probabilistic estimation results.
De Paola A, Fele F, Angeli D, et al., 2019, Distributed coordination of price-responsive electric loads: a receding horizon approach, 57th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 6033-6040, ISSN: 0743-1546
This paper presents a novel receding horizon framework for the power scheduling of flexible electric loads performing heterogeneous periodic tasks. The loads are characterized as price-responsive agents and their interactions are modelled through an infinite-time horizon aggregative game. A distributed control strategy based on iterative better-response updates is proposed to coordinate the loads, proving its convergence and global optimality with Lyapunov stability tools. Robustness with respect to variations in the number and tasks of players is also ensured. Finally, the performance of the control scheme is evaluated in simulation, coordinating the daily battery charging of a large fleet of electric vehicles.
Sun M, Teng F, Zhang X, et al., 2019, Data-driven representative day selection for investment decisions: a cost-oriented approach, IEEE Transactions on Power Systems, Vol: 34, Pages: 2925-2936, ISSN: 0885-8950
Power system investment planning problems become intractable due to the vast variability that characterizes system operation and the increasing complexity of the optimization model to capture the characteristics of renewable energy sources (RES). In this context, making optimal investment decisions by considering every operating period is unrealistic and inefficient. The conventional solution to address this computational issue is to select a limited number of representative operating periods by clustering the input demand-generation patterns while preserving the key statistical features of the original population. However, for an investment model that contains highly complex nonlinear relationship between input data and optimal investment decisions, selecting representative periods by relying on only input data becomes inefficient. This paper proposes a novel investment costoriented representative day selection framework for large scale multi-spacial investment problems, which performs clustering directly based on the investment decisions for each generation technology at each location associated with each individual day. Additionally, dimensionality reduction is performed to ensure that the proposed method is feasible for large-scale power systems and high-resolution input data. The superior performance of the proposed method is demonstrated through a series of case studies with different levels of modeling complexity.
Hussain A, Rousis AO, Konstantelos I, et al., 2019, Impact of uncertainties on resilient operation of microgrids: A data-driven approach, IEEE Access, Vol: 7, Pages: 14924-14937, ISSN: 2169-3536
In this paper, the impact of uncertainties in loads, renewable generation, market price signals, and event occurrence time on the feasible islanding and survivability of microgrids is analyzed. A data-driven approach is proposed for estimating the maximum deviation level of uncertain parameters dynamically based on historical data. Similarly, fragility curves are utilized for determining the preparation time for the potential events based on the estimated event occurrence time and physical constraints of the microgrid components. In addition, a resilience-oriented demand response program is proposed for enhancing the utilization of renewables and other available resources for reducing the load shedding during the emergency period. Finally, a resilience index is proposed for quantifying the benefits of the proposed method for the resilience-oriented operation of microgrids. In normal mode, the impact of event occurrence time and uncertainty level is analyzed via an adaptive robust optimization method. In emergency mode, 10 000 Monte Carlo scenarios of all the uncertain parameters are generated, and their impact on the operation cost, amount of load shed, and the range of the proposed resilience index are analyzed for each case.
Cremer J, Konstantelos I, Tindemans S, et al., 2019, Data-driven power system operation: Exploring the balance between cost and risk, IEEE Transactions on Power Systems, Vol: 34, Pages: 791-801, ISSN: 0885-8950
Supervised machine learning has been successfully used in the past to infer a system's security boundary by training classifiers (also referred to as security rules) on a large number of simulated operating conditions. Although significant research has been carried out on using classifiers for the detection of critical operating points, using classifiers for the subsequent identification of suitable preventive/corrective control actions remains underdeveloped. This paper focuses on addressing the challenges that arise when utilizing security rules for control purposes. The inherent trade-off between operating cost and security risk is explored in detail. To optimally navigate this trade-off, a novel approach is proposed that uses an ensemble learning method (AdaBoost) to infer a probabilistic description of a system's security boundary and Platt Calibration to correct the introduced bias. Subsequently, a general-purpose framework for building probabilistic and disjunctive security rules of a system's secure operating domain is developed that can be embedded within classic operation formulations. Through case studies on the IEEE 39-bus system, it is showcased how security rules can be efficiently utilized to optimally operate the system under multiple uncertainties while respecting a user-defined cost-risk balance. This is a fundamental step towards embedding data-driven models within classic optimisation approaches.
Zhang X, Strbac G, Shah N, et al., 2019, Whole-system assessment of the benefits of integrated electricity and heat system, IEEE Transactions on Smart Grid, Vol: 10, Pages: 1132-1145, ISSN: 1949-3061
The interaction between electricity and heat systems will play an important role in facilitating the cost effective transition to a low carbon energy system with high penetration of renewable generation. This paper presents a novel integrated electricity and heat system model in which, for the first time, operation and investment timescales are considered while covering both the local district and national level infrastructures. This model is applied to optimize decarbonization strategies of the UK integrated electricity and heat system, while quantifying the benefits of the interactions across the whole multi-energy system, and revealing the trade-offs between portfolios of (a) low carbon generation technologies (renewable energy, nuclear, CCS) and (b) district heating systems based on heat networks (HN) and distributed heating based on end-use heating technologies. Overall, the proposed modeling demonstrates that the integration of the heat and electricity system (when compared with the decoupled approach) can bring significant benefits by increasing the investment in the heating infrastructure in order to enhance the system flexibility that in turn can deliver larger cost savings in the electricity system, thus meeting the carbon target at a lower whole-system cost.
Konstantelos I, Sun M, Tindemans S, et al., 2019, Using vine copulas to generate representative system states for machine learning, IEEE Transactions on Power Systems, Vol: 34, Pages: 225-235, ISSN: 0885-8950
The increasing uncertainty that surrounds electricity system operation renders security assessment a highly challenging task; the range of possible operating states expands, rendering traditional approaches based on heuristic practices and ad hoc analysis obsolete. In turn, machine learning can be used to construct surrogate models approximating the system's security boundary in the region of operation. To this end, past system history can be useful for generating anticipated system states suitable for training. However, inferring the underlying data model, to allow high-density sampling, is problematic due to the large number of variables, their complex marginal probability distributions and the non-linear dependence structure they exhibit. In this paper we adopt the C-Vine pair-copula decomposition scheme; clustering and principal component transformation stages are introduced, followed by a truncation to the pairwise dependency modelling, enabling efficient fitting and sampling of large datasets. Using measurements from the French grid, we show that a machine learning training database sampled from the proposed method can produce binary security classifiers with superior predictive capability compared to other approaches.
Fatouros P, Konstantelos I, Papadaskalopoulos D, et al., 2019, Stochastic Dual Dynamic Programming for Operation of DER Aggregators Under Multi-Dimensional Uncertainty, IEEE Transactions on Sustainable Energy, Vol: 10, Pages: 459-469, ISSN: 1949-3029
The operation of aggregators of distributed energy resources (DER) is highly complex, since it entails the optimal coordination of a diverse portfolio of DER under multiple sources of uncertainty. The large number of possible stochastic realizations that arise, can lead to complex operational models that become problematic in real-time market environments. Previous stochastic programming approaches resort to two-stage uncertainty models and scenario reduction techniques to preserve the tractability of the problem. However, two-stage models cannot fully capture the evolution of uncertain processes and the a priori scenario selection can lead to suboptimal decisions. In this context, this paper develops a novel stochastic dual dynamic programming (SDDP) approach which does not require discretization of either the state space or the uncertain variables and can be efficiently applied to a multi-stage uncertainty model. Temporal dependencies of the uncertain variables as well as dependencies among different uncertain variables can be captured through the integration of any linear multidimensional stochastic model, and it is showcased for a p-order vector autoregressive (VAR) model. The proposed approach is compared against a traditional scenario-tree-based approach through a Monte-Carlo validation process, and is demonstrated to achieve a better trade-off between solution efficiency and computational effort.
Rousis AO, Chairiman, Pipelzadeh Y, et al., 2018, Voltage Support from Distribution Level Resources in South-East England, IEEE-Power-and-Energy-Society General Meeting (PESGM), Publisher: IEEE, ISSN: 1944-9925
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