arXiv subjects

Goran Strbac

Publications and source records attributed to Goran Strbac.

46 records · Page 3Linked to original sources

arXiv2020

Economic Value of Inertia in Low-Carbon Power Systems

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.

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arXiv2020

Optimal Scheduling of Frequency Services Considering a Variable Largest-Power-Infeed-Loss

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.

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arXiv2019

Pricing inertia and Frequency Response with diverse dynamics in a Mixed-Integer Second-Order Cone Programming formulation

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.

math.OC↗

arXiv2019

Chance-Constrained Ancillary Service Specification for Heterogeneous Storage Devices

We present a method to find the maximum magnitude of any supply-shortfall service that an aggregator of energy storage devices is able to sell to a grid operator. This is first demonstrated in deterministic settings, then applied to scenarios in which device availabilities are stochastic. In this case we implement chance constraints on the inability to deliver as promised. We show a significant computational improvement in using our method in place of straightforward scenario simulation. As an extension, we present an approximation to this method which allows the determined fleet capability to be applied to any chosen service, rather than having to re-solve the chance-constrained optimisation each time.

math.OC↗

arXiv2019

Low-complexity control algorithm for decentralised demand response using thermostatic loads

Thermostatically controlled loads such as refrigerators are exceptionally suitable as a flexible demand resource. This paper derives a decentralised load control algorithm for refrigerators. It is adapted from an existing continuous time control approach, with the aim to achieve low computational complexity and an ability to handle discrete time steps of variable length -- desirable features for embedding in appliances and high-throughput simulations. Simulation results of large populations of heterogeneous appliances illustrate the accurate aggregate control of power consumption and high computational efficiency.

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arXiv2019

Simultaneous Scheduling of Multiple Frequency Services in Stochastic Unit Commitment

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.

math.OC↗

arXiv2019

Capacity Value of Interconnection Between Two Systems

Concerns about system adequacy have led to the establishment of capacity mechanisms in a number of regulatory areas. Against this background, it is essential to accurately quantify the contribution to security of supply that results from interconnectors to neighbouring systems. This paper introduces a definition of capacity value for interconnection between two systems in the form of a capacity allocation curve. Four power flow policies are proposed to encompass the full range of possible market outcomes that may affect the capacity value. A convolution-based method is presented to efficiently compute and compare capacity allocation curves, and it is applied to a model system that is inspired by Great Britain's interconnection with the continental Europe. The results indicate areas of interest for the coordination of capacity mechanisms.

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arXiv2018

Sample-Derived Disjunctive Rules for Secure Power System Operation

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.

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arXiv2017

Robust estimation of risks from small samples

Data-driven risk analysis involves the inference of probability distributions from measured or simulated data. In the case of a highly reliable system, such as the electricity grid, the amount of relevant data is often exceedingly limited, but the impact of estimation errors may be very large. This paper presents a robust nonparametric Bayesian method to infer possible underlying distributions. The method obtains rigorous error bounds even for small samples taken from ill-behaved distributions. The approach taken has a natural interpretation in terms of the intervals between ordered observations, where allocation of probability mass across intervals is well-specified, but the location of that mass within each interval is unconstrained. This formulation gives rise to a straightforward computational resampling method: Bayesian Interval Sampling. In a comparison with common alternative approaches, it is shown to satisfy strict error bounds even for ill-behaved distributions.

stat.ME↗

arXiv2016

A baseline-free method to identify responsive customers on dynamic time-of-use tariffs

Dynamic time-of-use tariffs incentivise changes in electricity consumption. This paper presents a non-parametric method to retrospectively analyse consumption data and quantify the significance of a customer's observed response to a dynamic price signal without constructing a baseline demand model. If data from a control group is available, this can be used to infer customer responsiveness - individually and collectively - on an absolute scale. The results are illustrated using data from the Low Carbon London project, which included the UK's first dynamic time-of-use pricing trial.

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