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Register a new userAssessing Energy Storage Requirements Based on Accepted Risks
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Added by
Michael P. Evans
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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This paper presents a framework for deriving the storage capacity that an electricity system requires in order to satisfy a chosen risk appetite. The framework takes as inputs user-defined event categories, parameterised by peak power-not-served, acceptable number of events per year and permitted probability of exceeding these constraints, and returns as an output the total capacity of storage that is needed. For increased model accuracy, our methodology incorporates multiple nodes with limited transfer capacities, and we provide a foresight-free dispatch policy for application to this setting. Finally, we demonstrate the chance-constrained capacity determination via application to a model of the British network.
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Energy storage is expected to play an increasingly important role in mitigating variations that come along with the growing penetration of renewable energy. In this paper, we study the optimal bidding of an energy storage unit in a semi-centralized market. The energy storage unit offers its available storage capacity and maximum charging/ discharging rate to the operator; then the operator clears the real-time market by minimizing the total cost. The energy storage unit is paid/ charged at locational marginal price (LMP). The problem casts down to a bilevel optimization problem with a mixed-integer lower-level. An improved surrogate-based method with the combined spatial-temporal entropy term is developed to solve this problem. Numerical examples demonstrate the scalability, efficiency, and accuracy of the proposed method.
French regulation allows consumers in low-voltage networks to form collectives to produce, share, and consume local energy under the collective self-consumption framework. A natural consequence of collectively-owned generation projects is the need to allocate production among consumers. In long-term plans, production allocation determines each of the consumers benefits of joining the collective. In the short-term, energy should be dynamically allocated to reflect operation. This paper presents a framework that integrates long and short-term planning of a collective that shares a solar plus energy storage system. In the long-term planning stage, we maximize the collectives welfare and equitably allocate expected energy to each consumer. For operation, we propose a model predictive control algorithm that minimizes short-term costs and allocates energy to each consumer on a 30-minute basis (as required by French regulation). We adjust the energy allotment ex-post operation to reflect the materialization of uncertainty. We present a case study where we showcase the framework for a 15 consumer collective.
This paper considers the optimal dispatch of energy-constrained heterogeneous storage units to maximise security of supply. A policy, requiring no knowledge of the future, is presented and shown to minimise unserved energy during supply-shortfall events, regardless of the supply and demand profiles. It is accompanied by a graphical means to rapidly determine unavoidable energy shortfalls, which can then be used to compare different device fleets. The policy is well-suited for use within the framework of system adequacy assessment; for this purpose, a discrete time optimal policy is conceived, in both analytic and algorithmic forms, such that these results can be applied to discrete time systems and simulation studies. This is exemplified via a generation adequacy study of the British system.
Optimal power flow (OPF) is the fundamental mathematical model in power system operations. Improving the solution quality of OPF provide huge economic and engineering benefits. The convex reformulation of the original nonconvex alternating current OPF (ACOPF) model gives an efficient way to find the global optimal solution of ACOPF but suffers from the relaxation gaps. The existence of relaxation gaps hinders the practical application of convex OPF due to the AC-infeasibility problem. We evaluate and improve the tightness of the convex ACOPF model in this paper. Various power networks and nodal loads are considered in the evaluation. A unified evaluation framework is implemented in Julia programming language. This evaluation shows the sensitivity of the relaxation gap and helps to benchmark the proposed tightness reinforcement approach (TRA). The proposed TRA is based on the penalty function method which penalizes the power loss relaxation in the objective function of the convex ACOPF model. A heuristic penalty algorithm is proposed to find the proper penalty parameter of the TRA. Numerical results show relaxation gaps exist in test cases especially for large-scale power networks under low nodal power loads. TRA is effective to reduce the relaxation gap of the convex ACOPF model.
The impasse surface is an important concept in the differential-algebraic equation (DAE) model of power systems, which is associated with short-term voltage collapse. This paper establishes a necessary condition for a system trajectory hitting the impasse surface. The condition is in terms of admittance matrices regarding the power network, generators and loads, which specifies the pattern of interaction between those system components that can induce voltage collapse. It applies to generic DAE models featuring high-order synchronous generators, static load components, induction motors and a lossy power network. We also identify a class of static load parameters that prevents power systems from hitting the impasse surface; this proves a conjecture made by Hiskens that has been unsolved for decades. Moreover, the obtained results lead to an early indicator of voltage collapse and a novel viewpoint that inductive compensation has a positive effect on preventing short-term voltage collapse, which are verified via numerical simulations.
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