51 results found
Luo J, Bu S, Teng F, 2020, An optimal modal coordination strategy based on modal superposition theory to mitigate low frequency oscillation in FCWG penetrated power systems, International Journal of Electrical Power & Energy Systems, Vol: 120, Pages: 1-11, ISSN: 0142-0615
Full converter-based wind power generation (FCWG, e.g. permanent magnet synchronous generator (PMSG)) becomes prevalent in power electronics dominated multi-machine power system (MMPS). With flexibly modified FCWG oscillation modes (FOMs), FCWG has the potential to actuate conducive dynamic interactions with electromechanical oscillation modes (EOMs) of MMPS. In this paper, a mathematical model of FCWG and MMPS is firstly derived to examine the dynamic interactions. Then a novel modal superposition theory is proposed to classify the modal interactions between FOMs and EOMs in the complex plane for the first time. The modal coupling mechanism is graphically visualized to investigate the dynamic interactions, and the eigenvalue shift index is proposed to quantify the dynamic interaction impact on critical EOM. Based on different manifestos in modal coupling mechanism and eigenvalue shift index, a novel methodology to optimize the dynamic interactions between the FCWG and MMPS is designed within the existing control frame. The optimized dynamic interactions (i.e. modal counteraction) can significantly enhance the LFO stability of MMPS, effectiveness of which is verified by both modal analysis and time domain simulations.
Guo J, Badesa Bernardo L, Teng F, et al., Value of Point-of-load Voltage Control for Enhanced Frequency Response in Future GB Power System, IEEE Transactions on Smart Grid, ISSN: 1949-3053
Shen F, Wu Q, Xu Y, et al., 2020, Hierarchical service restoration scheme for active distribution networks based on ADMM, International Journal of Electrical Power & Energy Systems, Vol: 118, Pages: 1-10, ISSN: 0142-0615
Effective self-healing schemes enhance the resilience of active distribution networks (ADNs). As a critical part of self-healing, service restoration aims to restore outage areas with minimal un-supplied demands. With the increasing complexity and size of ADNs, distribution system operators (DSOs) face a more complicated service restoration problem. Thus, it is important to obtain optimal service restoration plans and reduce computational complexity. To achieve this goal, a hierarchical service restoration scheme is proposed to obtain service restoration plans based on the alternating direction method of multipliers (ADMM). The optimal service restoration problem is formulated as a mixed-integer linear programming (MILP) model considering the switching sequence, distributed generation (DG) units and controllable loads, and is solved using the ADMM-based algorithm in a hierarchical manner. In the proposed scheme, each zone of the ADN has a local service restoration controller solving its sub-problem with information from a central service restoration controller. The central controller solves a global coordination problem with information from all the zones. Three case studies were conducted with the 44-node test system, modified IEEE 123-node system and Brazil 948-node system. The results show that the proposed hierarchical service restoration can obtain optimal service restoration plans and reduce computational complexity. Moreover, computation time can be reduced substantially by using the proposed hierarchical scheme for large-scale ADNs.
Cheng C, Ma G, Zhang Y, et al., 2020, A Deep Learning-Based Remaining Useful Life Prediction Approach for Bearings, IEEE/ASME Transactions on Mechatronics, Vol: 25, Pages: 1243-1254, ISSN: 1083-4435
Zhang Z, Du E, Teng F, et al., 2020, Modeling frequency dynamics in unit commitment with a high share of renewable energy, IEEE Transactions on Power Systems, Pages: 1-1, ISSN: 0885-8950
The power system inertia is gradually decreasing with the growing share of variable renewable energy (VRE). This may jeopardize the frequency dynamics and challenges the secure operation of power systems. In this paper, the concept of frequency security margin is proposed to quantify the system frequency regulation ability under contingency. It is defined as the maximum power imbalance that the system can tolerate while keeping frequency within the tolerable frequency range. A frequency constrained unit commitment (FCUC) model considering frequency security margin is proposed. Firstly, the analytical formulation of system frequency nadir is derived while considering both the frequency regulation characteristics of the thermal generators and the frequency support from VRE plants. Then, the frequency security margin is analytically formulated and piecewise linearized. A novel FCUC model is proposed by incorporating linear frequency security constraints into the traditional unit commitment model. Case studies on a modified IEEE RTS-79 system and HRP-38 system are provided to verify the effectiveness of the proposed FCUC model. The impacts of VRE penetration on system frequency security are analyzed using frequency security margin.
Chu Z, Markovic U, Hug G, et al., 2020, Towards optimal system scheduling with synthetic inertia provision from wind turbines, IEEE Transactions on Power Systems, Pages: 1-1, ISSN: 0885-8950
The undergoing transition from conventional to converter-interfaced renewable generation leads to significant challenges in maintaining frequency stability due to declining system inertia. In this paper, a novel control framework for Synthetic Inertia (SI) provision from Wind Turbines (WTs) is proposed, which eliminates the secondary frequency dip and allows the dynamics of SI from WTs to be analytically integrated into the system frequency dynamics. Furthermore, analytical system frequency constraints with SI provision from WTs are developed and incorporated into a stochastic system scheduling model, which enables the provision of SI from WTs to be dynamically optimized on a system level. Several case studies are carried out on a Great Britain 2030 power system with different penetration levels of wind generation and inclusion of frequency response requirements in order to assess the performance of the proposed model and analyze the influence of the improved SI control scheme on the potential secondary frequency dip. The results demonstrate that the inclusion of SI provision from WTs into Unit Commitment (UC) can drastically impact the overall system costs.
Malekpour M, Azizipanah-Abarghooee R, Teng F, et al., 2020, Fast Frequency Response From Smart Induction Motor Variable Speed Drives, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 35, Pages: 997-1008, ISSN: 0885-8950
Badesa L, Teng F, Strbac G, 2020, Pricing inertia and Frequency Response with diverse dynamics in a Mixed-Integer Second-Order Cone Programming formulation, Applied Energy, Vol: 260, Pages: 1-11, ISSN: 0306-2619
Low levels of system inertia in power grids with significant penetration of non-synchronous Renewable Energy Sources (RES) have increased the risk of frequency instability. The provision of a certain type of ancillary services such as inertia and Frequency Response (FR) is needed at all times, to maintain system frequency within secure limits if the loss of a large power infeed were to occur. In this paper we propose a frequency-secured optimisation framework for the procurement of inertia and FR with diverse dynamics, which enables to apply a marginal-pricing scheme for these services. This pricing scheme, deduced from a Mixed-Integer Second-Order Cone Program (MISOCP) formulation that represents frequency-security constraints, allows for the first time to appropriately value multi-speed FR.
Chai Y, Xiang Y, Liu J, et al., 2020, Investment decision optimization for distribution network planning with correlation constraint, International Transactions on Electrical Energy Systems, ISSN: 2050-7038
With the increasing access of distributed generation (DG), the investment decision of distribution network (DN) has become a large‐scale portfolio optimization problem with various reconstruction strategies, which reduces the applicability of the traditional investment decision optimization model. Therefore, aiming at reliability of DN, a novel deep belief networks (DBN)‐based correlation constraint‐integrated investment decision model is proposed in this paper. With the DBN‐based correlation constraint replacing the nonlinear and nonconvex constraints in the traditional model, a new investment decision model is established aiming at maximizing the reliability index and minimizing the total investment cost. In this way, the effects of different reconstruction strategies can be analysed, from which the optimal investment reconstruction plans are identified. Finally, an example of a regional distribution network in a city is provided to verify the rapidity, feasibility, and effectiveness of the investment decision model.
Xiang Y, Jiang Z, Gu C, et al., 2019, Electric vehicle charging in smart grid: A spatial-temporal simulation method, Energy, Vol: 189, Pages: 1-12, ISSN: 0360-5442
Electric vehicles (EVs) play an important role in the future energy system. The large-scale adoption of moving EV load significantly accelerates the integration of transportation and distribution systems. The method to simulate the mobility and charging of a single or aggregated EVs is the key to analyze EVs’ flexibility on the operation of distribution network. Considering the integrated impacts from both the transportation and power systems, and the uncertainty of user’s driving behavior and charging intention, this paper proposes a spatial-temporal simulation method based on the vehicle-transportation-grid trajectory. The trajectory can not only describe the destination location and time like the trip chain, but also give the key information including the driving path in a whole travel process. The driving, parking, and charging are analyzed by the proposed spatial-temporal simulation method. It models the driving behavior based on statistical results and transportation systems, EV energy consumption pattern based on battery energy, and the charging demand based on the user’s subjective intention at the coupled systems. Finally, a 30-node transportation system is developed and integrated with a 33-bus distribution network to illustrate the proposed method. Two typical days, “workday” and “holiday”, are simulated and compared under different EV penetration levels (0%, 20%, 50% and 100%), different trip chain ratio (the ratio of 3-trip chains is 50%, 70%, 90%) to demonstrate the effectiveness of the spatial-temporal simulation method.
Sun M, Wang Y, Teng F, et al., 2019, Clustering-based residential baseline estimation: a probabilistic perspective, IEEE Transactions on Smart Grid, Vol: 10, Pages: 6014-6028, 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.
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.
Chu Z, Teng F, 2019, Assessment of Frequency Support from Wind Turbines under Alternative Control Schemes
© 2019 IEEE. The increasing penetration of wind generation in power grid introduces challenges for the system operation and stability, highlighting the importance of the frequency support from wind turbines (WTs). In this context, this paper proposes a novel control framework for the frequency support from WTs in power systems. The recovery effect of WTs associated with the frequency support delivering is explicitly included in the system frequency dynamics such that the secondary frequency dip can be eliminated. Various control schemes (constant power, inertia response and droop response) are analyzed and compared in terms of the effectiveness to replace primary frequency response(PFR) while maintaining the system frequency nadir above the limit. The proposed control design is verified through Matlab/Simulink simulations. A significant reduction of PFR requirement is observed especially in a system with high penetration of wind generation.
Camal S, Teng F, Michiorri A, et al., 2019, Scenario generation of aggregated wind, photovoltaics and small hydro production for power systems applications, Applied Energy, Vol: 242, Pages: 1396-1406, ISSN: 0306-2619
This paper proposes a methodology for an efficient generation of correlated scenarios of Wind, Photovoltaics (PV) and small Hydro production considering the power system application at hand. The merits of scenarios obtained from a direct probabilistic forecast of the aggregated production are compared with those of scenarios arising from separate production forecasts for each energy source, the correlations of which are modeled in a later stage with a multivariate copula. It is found that scenarios generated from separate forecasts reproduce globally better the variability of a multi-source aggregated production. Aggregating renewable power plants can potentially mitigate their uncertainty and improve their reliability when they offer regulation services. In this context, the first application of scenarios consists in devising an optimal day-ahead reserve bid made by a Wind-PV-Hydro Virtual Power Plant (VPP). Scenarios are fed into a two-stage stochastic optimization model, with chance-constraints to minimize the probability of failing to deploy reserve in real-time. Results of a case study show that scenarios generated by separately forecasting the production of each energy source leads to a higher Conditional Value at Risk than scenarios from direct aggregated forecasting. The alternative forecasting methods can also significantly affect the scheduling of future power systems with high penetration of weather-dependent renewable plants. The generated scenarios have a second application here as the inputs of a two-stage stochastic unit commitment model. The case study demonstrates that the direct forecast of aggregated production can effectively reduce the system operational cost, mainly through better covering the extreme cases. The comprehensive application-based assessment of scenario generation methodologies in this paper informs the decision-makers on the optimal way to generate short-term scenarios of aggregated RES production according to their risk aversion
Wang Y, Chen Q, Zhang N, et al., 2019, Fusion of the 5G Communication and the Ubiquitous Electric Internet of Things: Application Analysis and Research Prospects, Dianwang Jishu/Power System Technology, Vol: 43, Pages: 1575-1585, ISSN: 1000-3673
© 2019, Power System Technology Press. All right reserved. The ubiquitous electric Internet of Things (IoT) is a concrete manifestation of the IoT in the power industry. It is a deep integration of interconnected power network and communication network, and an important measure to implement the Energy Internet. The fifth generation mobile communication (5G communication) is favored by all walks of life because of its advantages of high bandwidth, low delay and low power consumption. It will also play an important role in the ubiquitous electric IoT. This paper discusses the deep fusion of the 5G communication technology and the ubiquitous electric IoT. Specifically, the potential application scenarios of 5G communication in ubiquitous electric IoT are analyzed. In addition, the key technologies of the 5G communication that support ubiquitous electric IoT are also summarized. Since the base stations of 5G communication network are dense and the energy consumption is large in the future, how ubiquitous electric IoT supports 5G communication network and the coordinated interaction between the two networks are also studied. Finally, future research on the fusion of the 5G communication technology and the ubiquitous electric IoT are prospected.
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.
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.
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.
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.
Pudjianto D, Papadaskalopoulos D, Moreira R, et al., 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., Flexibility Potential of Industrial Electricity Demand: Insights from the H2020 IndustRE project, the 11th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion
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
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
Teng F, Dupin R, Kariniotakis G, et al., 2018, Understanding the benefits of dynamic line rating under multiple sources of uncertainty, IEEE Transactions on Power Systems, Vol: 33, Pages: 3306-3314, ISSN: 0885-8950
This paper analyses the benefits of dynamic line rating (DLR) in the system with high penetration of wind generation. A probabilistic forecasting model for the line ratings is incorporated into a two-stage stochastic optimization model. The scheduling model, for the first time, considers the uncertainty associated with wind generation, line ratings and line outages to co-optimize the energy production and reserve holding levels in the scheduling stage as well as the re-dispatch actions in the real-time operation stage. Therefore, the benefits of higher utilization of line capacity can be explicitly balanced against the costs of increased holding and utilization of reserve services due to the forecasting error. The computational burden driven by the modelling of multiple sources of uncertainty is tackled by applying an efficient filtering approach. The case studies demonstrate the benefits of DLR in supporting costeffective integration of high penetration of wind generation into the existing network. We also highlight the importance of simultaneously considering the multiple sources of uncertainty in understanding the benefits of DLR. Furthermore, this paper analyses the impact of different operational strategies, the coordination among multiple flexible technologies and installed capacity of wind generation on the benefits of DLR.
Teng F, Pudjianto D, Aunedi M, et al., 2018, Assessment of Future Whole-System Value of Large-Scale Pumped Storage Plants in Europe, Energies, Vol: 11, ISSN: 1996-1073
This paper analyses the impacts and benefits of the pumped storage plant (PSP) and its upgrade to variable speed on generation and transmission capacity requirements, capital costs, system operating costs and carbon emissions in the future European electricity system. The combination of a deterministic system planning tool, Whole-electricity System Investment Model (WeSIM), and a stochastic system operation optimisation tool, Advanced Stochastic Unit Commitment (ASUC), is used to analyse the whole-system value of PSP technology and to quantify the impact of European balancing market integration and other competing flexible technologies on the value of the PSP. Case studies on the Pan-European system demonstrate that PSPs can reduce the total system cost by up to €13 billion per annum by 2050 in a scenario with a high share of renewables. Upgrading the PSP to variable-speed drive enhances its long-term benefits by 10–20%. On the other hand, balancing market integration across Europe may potentially reduce the overall value of the variable-speed PSP, although the effect can vary across different European regions. The results also suggest that large-scale deployment of demand-side response (DSR) leads to a significant reduction in the value of PSPs, while the value of PSPs increases by circa 18% when the total European interconnection capacity is halved. The benefit of PSPs in reducing emissions is relatively negligible by 2030 but constitutes around 6–10% of total annual carbon emissions from the European power sector by 2050.
Badesa L, Teng F, Strbac G, 2018, Economic value of inertia in low-carbon power systems, 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Publisher: IEEE
Most renewable energy sources (RES) do not provide any inertial response. Their integration in a power grid implies a highly reduced level of system inertia, which leads to a deteriorated frequency performance. Then, the requirement for frequency response is significantly increased in order to maintain frequency security. Alternatively, enhanced provision of inertia from auxiliary sources may alleviate this problem. However, the benefits of inertia provision are not yet fully understood. In this paper, an inertia-dependent Stochastic Unit Commitment (SUC) tool is applied to quantify the economic value of inertia. The results demonstrate that enhanced provision of inertia would lead to significant economic savings, although these savings vary under different system conditions. These results should be brought to the attention of both market operators and investors, in order to inform the design of an ancillary-services market for inertia and the investment in auxiliary provision of inertia.
Teng F, Aunedi M, Strbac G, et al., 2018, Provision of ancillary services in future low-carbon UK electricity system, IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Publisher: IEEE, ISSN: 2165-4816
Integration of intermittent RES into the electricity system imposes a considerable demand for additional flexibility. This paper analyses the challenges on the provision of ancillary services and potential solutions from emerging flexible technologies (including flexible generation, energy storage, demand side response and interconnection) in the future UK electricity system. The results suggest that the cost of reserve and response services in 2030 may increase up to 1.23 B£ and 1.04 B£, respectively. Alternative flexible technologies have been demonstrated to play an important role in the provision of ancillary services, although the benefits vary among different technologies. Furthermore, these flexible technologies can also reduce carbon emission and hence the required amount of high-cost low-carbon generation to achieve the same carbon target.
Sun M, Teng F, Konstantelos I, et al., 2018, An objective-based scenario selection method for transmission network expansion planning with multivariate stochasticity in load and renewable energy sources, Energy, Vol: 145, Pages: 871-885, ISSN: 0360-5442
Transmission Network Expansion Planning (TNEP) in modern electricity systems is carried out on a cost-benefit analysis basis; the planner identifies investments that maximize the social welfare. As the integration of Renewable Energy Sources (RES) increases, there is a real challenge to accurately capture the vast variability that characterizes system operation within a planning problem. Conventional approaches that rely on a large number of scenarios for representing the variability of operating points can quickly lead to computational issues. An alternative approach that is becoming increasingly necessary is to select representative scenarios from the original population via clustering techniques. However, direct clustering of operating points in the input domain may not capture characteristics which are important for investment decision-making. This paper presents a novel objective-based scenario selection framework for TNEP to obtain optimal investment decisions with a significantly reduced number of operating states. Different clustering frameworks, clustering variable s and clustering techniques are compared to determine the most appropriate approach. The superior performance of the proposed framework is demonstrated through a case study on a modified IEEE 118-bus system.
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