Publications
541 results found
Giannelos S, Konstantelos I, Strbac G, 2019, Investment Model for Cost-effective Integration of Solar PV Capacity under Uncertainty using a Portfolio of Energy Storage and Soft Open Points, IEEE Milan PowerTech Conference, Publisher: IEEE
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- Citations: 7
Fu P, Pudjianto D, Zhang X, et al., 2019, Evaluating Strategies for Decarbonising the Transport Sector in Great Britain, IEEE Milan PowerTech Conference, Publisher: IEEE
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- Citations: 2
De Paola A, Trovato V, Angeli D, et al., 2019, Value of Thermostatic Loads in Energy/Frequency Response Markets: a Mean Field Game Approach, IEEE Milan PowerTech Conference, Publisher: IEEE
Rousis AO, Boonsiri P, Strbac G, 2019, Utilization of an Urban AC Microgrid for Improving Voltages Across a Distribution System, 2nd International Conference on Smart Energy Systems and Technologies (SEST), Publisher: IEEE
Evans MP, Angeli D, Strbac G, et al., 2019, Chance-Constrained Ancillary Service Specification for Heterogeneous Storage Devices, Publisher: IEEE
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- Citations: 3
Badesa L, Teng F, Strbac G, 2018, Optimal scheduling of frequency services considering a variable largest-power-infeed-loss, 2018 IEEE Power and Energy Society General Meeting, Publisher: IEEE, ISSN: 1944-9925
Low levels of inertia due to increasing renewable penetration bring several challenges, such as the higher need for Primary Frequency Response (PFR). A potential solution to mitigate this problem consists on reducing the largest possible power loss in the grid. This paper develops a novel modelling framework to analyse the benefits of such approach.A new frequency-constrained Stochastic Unit Commitment (SUC) is proposed here, which allows to dynamically reduce the largest possible loss in the optimisation problem. Furthermore, the effect of load damping is included by means of an approximation, while its effect is typically neglected in previous frequency-secured-UC studies. Through several case studies, we demonstrate that reducing the largest loss could significantly decrease operational cost and carbon emissions in the future Great Britain's grid.
Pipelzadeh Y, Moreno R, Chaudhuri B, et al., 2018, Corrective Control With Transient Assistive Measures: Value Assessment for Great Britain Transmission System, IEEE-Power-and-Energy-Society General Meeting (PESGM), Publisher: IEEE, ISSN: 1944-9925
Konstantelos I, Jamgotchian G, Tindemans S, et al., 2018, Implementation of a Massively Parallel Dynamic Security Assessment Platform for Large-Scale Grids, IEEE-Power-and-Energy-Society General Meeting (PESGM), Publisher: IEEE, ISSN: 1944-9925
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
Gong X, De Paola A, Angeli D, et al., 2018, A distributed price-based strategy for flexible demand coordination in multi-area, IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Publisher: IEEE, ISSN: 2165-4816
This paper presents a novel distributed control strategy for large-scale deployment of price-responsive flexible demand. Differently from previous theoretical studies on the subject, the proposed analysis explicitly models multi-area systems, accounting for transmission lines of limited capacity and different locational marginal prices (LMP) throughout the network. A game-theoretic framework is adopted, designing a demand coordination scheme that converges to a stable market configuration (characterized as an aggregative equilibrium) through iterative price broadcasts. The performance of the proposed control strategy, that also ensures flattened generation profiles and reduced generation costs, is evaluated in simulation on a five-bus power system.
de Paola A, Angeli D, Strbac G, 2018, On distributed scheduling of flexible demand and Nash equilibria in the electricity market, Dynamic Games and Applications, Vol: 8, Pages: 761-798, ISSN: 2153-0785
This paper presents a novel game theory approach for large-scale deployment of price-responsive electrical appliances. In the proposed distributed control scheme, each appliance independently schedules its power consumption on the basis of a broadcast demand/price signal, aiming to complete its task at minimum cost. The conflicting interactions of the appliances, competing for power consumption at the cheapest hours of the day, are modelled through a differential game with a continuum of players, and efficient deployment of flexible demand is characterized as a Nash equilibrium. A novel approach is adopted to derive necessary and sufficient equilibrium conditions: intrinsic properties of the problem (price monotonicity, unidirectionality of power transfers) are exploited to perform an equilibrium study based on sublevel sets of the considered demand profiles. As a result, it is possible to determine for which penetration levels of flexible demand, types of appliances and inflexible demand profiles it is possible to achieve an equilibrium. Such stable configuration is achieved through the broadcast of a single demand/price signal and does not require iterated exchange of information between devices and coordinator. In addition, the global optimality of the equilibrium is proved, necessary conditions for Pareto optimality are derived, and a preliminary analysis of devices with partial time availability is carried out. The performance of the proposed control strategy is evaluated in simulation, considering realistic future scenarios of the UK power system with large penetration of flexible demand.
Greve T, Teng F, Pollitt MG, et al., 2018, A system operator's utility function for the frequency response market, Applied Energy, Vol: 231, Pages: 562-569, ISSN: 0306-2619
How can the electricity system operator determine the optimal quantity and quality of electricity ancillary services (such as frequency response) to procure in a market increasingly characterized by intermittent renewable electricity generation? The paper presents a system operator’s utility function to calculate the exchange rates in monetary values between different frequency response products in the electricity system. We then use the utility function in a two-sided Vickrey-Clarke-Groves (VCG) mechanism combined of two frequency response products – enhanced and primary – in the context of the system in Great Britain. This mechanism would allow the market to reveal to the system operator the welfare optimal mix of speed of frequency response and quantity to procure. We show that this mechanism is the efficient way to support new faster sources of frequency response, such as could be provided by grid scale batteries.
Papadaskalopoulos D, Moreira R, Strbac G, et al., 2018, Quantifying the potential economic benefits of flexible industrial demand in the European power system, IEEE Transactions on Industrial Informatics, Vol: 14, Pages: 5123-5132, ISSN: 1551-3203
The envisaged decarbonization of the European power system introduces complex techno-economic challenges to its operation and development. Demand flexibility can significantly contribute in addressing these challenges and enable a cost-effective transition to the low-carbon future. Although extensive previous work has analyzed the impacts of residential and commercial demand flexibility, the respective potential of the industrial sector has not yet been thoroughly investigated despite its large size. This paper presents a novel, whole-system modeling framework to comprehensively quantify the potential economic benefits of flexible industrial demand (FID) for the European power system. This framework considers generation, transmission and distribution sectors of the system, and determines the least-cost long-term investment and short-term operation decisions. FID is represented through a generic, process-agnostic model, which however accounts for fixed energy requirements and load recovery effects associated with industrial processes. The numerical studies demonstrate multiple significant value streams of FID in Europe, including capital cost savings by avoiding investments in additional generation and transmission capacity and distribution reinforcements, as well as operating cost savings by enabling higher utilization of renewable generation sources and providing balancing services.
De Paola A, Gong X, Angeli D, et al., 2018, Coordination of micro-storage devices in power grids: a multi-agent system approach for energy arbitrage, IEEE Conference on Control Technology and Applications (CCTA), Publisher: IEEE, Pages: 871-878
Sun M, Cremer J, Strbac G, 2018, A novel data-driven scenario generation framework for transmission expansion planning with high renewable energy penetration, Applied Energy, Vol: 228, Pages: 546-555, ISSN: 0306-2619
Transmission expansion planning (TEP) is facing unprecedented challenges with the rise of integrated renewable energy resources (RES), flexible load elements, and the potential electrification of transport and heat sectors. Under this reality, the inadequate information of the stochastic parameters’ behavior may lead to inefficient expansion decisions, especially in the context of very high renewable penetration. This paper proposes a novel data-driven scenario generation framework for the TEP problem to generate unseen but important load and wind power scenarios while capturing inter-spatial dependencies between loads and wind generation units’ output in various locations, using a vine-copula based high-dimensional stochastic variable modeling approach. The superior performance of the proposed model is demonstrated through a case study on a modified IEEE 118-bus system. The expected result of using the expected value problem solution (EEV) and the net benefits of transmission expansion (NBTE) are used as the evaluation metrics to quantitatively illustrate the advantages of the proposed approach. In addition, the case of very high wind penetration is carried out to further highlight the importance of the multivariate stochastic dependence of load and wind power generation. The results demonstrate that the proposed scenario generation method can result in near-optimal investment decisions for the TEP problem that make more net benefits than using limited number of historical data.
Pudjianto D, Papadaskalopoulos D, Moreira R, et al., 2018, Flexibility Potential of Industrial Electricity Demand: Insights from the H2020 IndustRE project, The 11th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion
Pudjianto D, Papadaskalopoulos D, Moreira R, et al., 2018, Flexibility Potential of Industrial Electricity Demand: Insights from the H2020 IndustRE project, the 11th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion
Oderinwale T, Papadaskalopoulos D, Ye Y, et al., 2018, Incorporating demand flexibility in strategic generation investment planning, 15th International Conference on the European Energy Market, Publisher: IEEE
The envisaged decarbonization of electricity systems has attracted significant interest around the role and value of demand flexibility.However, the impact of this flexibility on generation investments in the deregulated electricity industry setting remains a largely unexplored area, since previous relevant work neglectsthe time-coupling nature of demand shifting potentials.This paper addresses this challenge by proposing a strategic generation investment planning model expressing the decision making process of a self-interestedgeneration company and accounting for the time-coupling operational characteristics of demand flexibility. This model is formulated as a multi-period bi-level optimization problem, which is solved after converting it to a Mathematical Program with Equilibrium Constraints (MPEC).Case studies with the proposedmodel demonstrate that demand flexibility reduces the total generation capacity investment, enhances investments in baseload generation and yieldssignificant economic benefits in terms of total system costs and demand payments.
Georgiou S, Aunedi M, Strbac G, et al., 2018, Application of liquid-air and pumped-thermal electricity storage systems in low-carbon electricity systems, Heat Powered Cycles - HPC-2018
In this study, we considertwo medium-to large-scale electricity storage systems currently under development, namely ‘Liquid-Air Energy Storage’ (LAES) and ‘Pumped-Thermal Electricity Storage’ (PTES). Consistent thermodynamic models and costing methodologies for the twosystems are presented,with the objective of integrating the characteristics of these technologies intoa whole-electricity system assessment model,andassessingtheirsystem-levelvalue in different scenarios for power system decarbonisation.It is found that the value of storage variesgreatlydepending on the cumulative installed capacity of storage in the electrical system, withthe storage technologies providinggreater marginal benefits at low penetrations. Two carbon target scenarios showed similar results, with a limited effect of the carbon target on the system value of storage (althoughit is noted thatthis may change for even more ambitious carbon targets). On the other hand, the location and installed capacity of storage plants isfound to have a significantimpact on the system value and acceptable cost of thesetechnologies. The whole-system value of PTES was foundto be slightly higher than that of LAES, driven by a higher storage duration and efficiency,however, due to the higher power capital cost of PTES, this becomes less attractive for implementation at lower volumes than LAES.
Trovato V, Teng F, Strbac G, 2018, Role and Benefits of Flexible Thermostatically Controlled Loads in Future Low-Carbon Systems, IEEE TRANSACTIONS ON SMART GRID, Vol: 9, Pages: 5067-5079, ISSN: 1949-3053
Calvo JL, Tindemans SH, Strbac G, 2018, Risk-based method to secure power systems against cyber-physical faults with cascading impacts: a system protection scheme application, Journal of Modern Power Systems and Clean Energy, Vol: 6, Pages: 930-943, ISSN: 2196-5420
The utilization levels of the transmission network can be enhanced by the use of automated protection schemes that rapidly respond to disturbances. However, such corrective systems may suffer from malfunctions that have the potential to exacerbate the impact of the disturbance. This paper addresses the challenge of jointly optimizing the dispatch of generators and protection settings in this context. This requires a holistic assessment of the cyber (protection logic) and physical (network) systems, considering the failures in each part and their interplay. Special protection schemes are used as a prototypical example of such a system. An iterative optimization method is proposed that relies on power system response simulations in order to perform detailed impact assessments and compare candidate solutions. The candidate solutions are generated on the basis of a security-constrained dispatch that also secures the system against a set of cyber failure modes. A case study is developed for a generation rejection scheme on the IEEE reliability test system (RTS): candidate solutions are produced based on a mixed integer linear programming optimisation model, and loss-of-load costs are computed using a basic cascading outage algorithm. It is shown that the partial security approach is able to identify solutions that provide a good balance of operational costs and loss-of-load risks, both in a fixed dispatch and variable dispatch context.
Giannelos S, Konstantelos I, Strbac G, 2018, Option value of demand-side response schemes under decision-dependent uncertainty, IEEE Transactions on Power Systems, Vol: 33, Pages: 5103-5113, ISSN: 0885-8950
Uncertainty in power system planning problems can be categorized into two types: exogenous and endogenous (or decision-dependent) uncertainty. In the latter case, uncertainty resolution depends on a choice (the value of some decision variables), as opposed to the former case in which the uncertainty resolves automatically with the passage of time. In this paper, a novel stochastic multistage planning model is proposed that considers endogenous uncertainty around consumer participation in demand-side response (DSR) schemes. This uncertainty can resolve following DSR deployment in two possible ways: locally (at a single bus) and globally (across the entire system). The original formulation is decomposed with the use of Benders decomposition to improve computational performance. Two versions of Benders decomposition are applied: the classic version involving sequential implementation of all operational subproblems and a novel version, specific to problems with endogenous uncertainty, which allows for the parallel execution of only those operational subproblems that are guaranteed to have a unique contribution to the solution. Case studies on 11-bus and 123-bus systems illustrate the process of endogenous uncertainty resolution and underline the strategic importance of deploying DSR ahead of time.
De Paola A, Papadaskalopoulos D, Angeli D, et al., 2018, Investigating the social efficiency of merchant transmission planning through a non-cooperative game-theoretic framework, IEEE Transactions on Power Systems, Vol: 33, Pages: 4831-4841, ISSN: 0885-8950
Merchant transmission planning is considered as a further step towards the full liberalization of the electricity industry. However, previous modeling approaches do not comprehensively explore its social efficiency as they cannot effectively deal with a large number of merchant companies. This paper addresses this fundamental challenge by adopting a novel non-cooperative game-theoretic approach. Specifically, the number of merchant companies is assumed sufficiently large to be approximated as a continuum. This allows the derivation of mathematical conditions for the existence of a Nash Equilibrium solution of the merchant planning game. By analytically and numerically comparing this solution against the one obtained through the traditional centralized planning approach, the paper demonstrates that merchant planning can maximize social welfare only when the following conditions are satisfied: a) fixed investment costs are neglected and b) the network is radial and does not include any loops. Given that these conditions do not generally hold in reality, these findings suggest that even a fully competitive merchant transmission planning framework, involving the participation of a very large number of competing merchant companies, is not generally capable of maximizing social welfare.
Cremer JL, Konstantelos I, Strbac G, et al., 2018, Sample-derived disjunctive rules for secure power system operation, International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Publisher: IEEE
Machine learning techniques have been used in the past using Monte Carlo samples to construct predictors of the dynamic stability of power systems. In this paper we move beyond the task of prediction and propose a comprehensive approach to use predictors, such as Decision Trees (DT), within a standard optimization framework for pre- and post-fault control purposes. In particular, we present a generalizable method for embedding rules derived from DTs in an operation decision-making model. We begin by pointing out the specific challenges entailed when moving from a prediction to a control framework. We proceed with introducing the solution strategy based on generalized disjunctive programming (GDP) as well as a two-step search method for identifying optimal hyper-parameters for balancing cost and control accuracy. We showcase how the proposed approach constructs security proxies that cover multiple contingencies while facing high-dimensional uncertainty with respect to operating conditions with the use of a case study on the IEEE 39-bus system. The method is shown to achieve efficient system control at a marginal increase in system price compared to an oracle model.
Oulis Rousis A, Tzelepis D, Konstantelos I, et al., 2018, Design of a Hybrid AC/DC Microgrid Using HOMER Pro: Case Study on an Islanded Residential Application, Inventions, Vol: 3, ISSN: 2411-5134
This paper is concerned with the design of an autonomous hybrid alternating current/direct current (AC/DC) microgrid for a community system, located on an island without the possibility of grid connection. It is comprised of photovoltaic (PV) arrays and a diesel generator, AC loads, and battery energy storage devices for ensuring uninterruptible power supply during prolonged periods of low sunshine. A multi-objective, non-derivative optimisation is considered in this residential application; the primary objective is the system cost minimisation, while it is also required that no load shedding is allowed. Additionally, the CO2 emissions are calculated to demonstrate the environmental benefit the proposed system offers. The commercial software, HOMER Pro, is utilised to identify the least-cost design among hundreds of options and simultaneously satisfy the secondary objective. A sensitivity analysis is also performed to evaluate design robustness against the uncertainty pertaining to fuel prices and PV generation. Finally, an assessment of the capabilities of the utilised optimisation platform is conducted, and a theoretical discussion sheds some light on the proposal for an enhanced design tool addressing the identified issues.
Konstantelos I, Strbac G, 2018, Capacity value of energy storage in distribution networks, Journal of Energy Storage, Vol: 18, Pages: 389-401, ISSN: 2352-152X
Security of supply in electricity distribution networks has been traditionally delivered by conventional assets such as transformers and circuits to supply energy to consumers. Although non-network solutions, such as energy storage (ES), can also be used to provide security of supply by carrying out peak shaving and maintaining supply for the duration of a network outage, present network design standards do not provide a framework for quantifying their security contribution and corresponding capacity value. Given the fundamentally different operating principles of ES, it is imperative to develop novel methodologies for assessing its contribution to security of supply and enable a level playing field to be established for future network planning. To this end, a novel probabilistic methodology based on chronological Monte Carlo simulations is developed for computing the Effective Load Carrying Capability (ELCC) of an energy storage plant. Substantial computational speed-up is achieved through event-based modelling and decomposing between energy and power constraints. The paper undertakes, for the first time, the in-depth analysis of key factors that can affect ES security contribution; plant and network outage frequency and duration, network redundancy level, demand shape, islanding operation capability and ES availability. ES capacity value is shown to decrease in networks with an unreliable connection to the grid; time to restore supply is shown to be more important that frequency of faults. Capacity value increases in cases of peaky demand profiles, while the ability to operate in islanded conditions is shown to be a critical factor. These findings highlight the need for sophisticated network design standards. The proposed methodology enables planners to consider ES solutions and allows network and non-network assets to comp
Wang S, Wang K, Teng F, et al., 2018, An affine arithmetic-based multi-objective optimization method for energy storage systems operating in active distribution networks with uncertainties, APPLIED ENERGY, Vol: 223, Pages: 215-228, ISSN: 0306-2619
Zhang X, Strbac G, Teng F, et al., 2018, Economic assessment of alternative heat decarbonisation strategies through coordinated operation with electricity system - UK case study, APPLIED ENERGY, Vol: 222, Pages: 79-91, ISSN: 0306-2619
Yang Y, Hao J, Wang Z, et al., 2018, Recurrent Deep Multiagent Q-Learning for Autonomous Agents in Future Smart Grid, 17th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), Publisher: ASSOC COMPUTING MACHINERY, Pages: 2136-2138
Moreno Vieyra R, 2018, Coordination Strategies for Securing AC/DC FlexibleTransmission Networks With Renewables, IEEE Transactions on Power Systems, ISSN: 0885-8950
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