Stochastic Hosting Capacity in LV Distribution Networks

Hosting capacity is defined as the level of penetration that a particular technology can connect to a distribution network without causing power quality problems. In this work, we study the impact of solar photovoltaics (PV) on voltage rise. In most cases, the locations and sizes of the PV are not known in advance, so hosting capacity must be considered as a random variable. Most hosting capacity methods study the problem considering a large number of scenarios, many of which provide little additional information. We overcome this problem by studying only cases where voltage constraints are active, with results illustrating a reduction in the number of scenarios required by an order of magnitude. A linear power flow model is utilised for this task, showing excellent performance. The hosting capacity is finally studied as a function of the number of generators connected, demonstrating that assumptions about the penetration level will have a large impact on the conclusions drawn for a given network

Conic optimisation for electric vehicle station smart charging with battery voltage constraints

This paper proposes a new convex optimisation strategy for coordinating electric vehicle charging, which accounts for battery voltage rise, and the associated limits on maximum charging power. Optimisation strategies for coordinating electric vehicle charging commonly neglect the increase in battery voltage which occurs as the battery is charged. However, battery voltage rise is an important consideration, since it imposes limits on the maximum charging power. This is particularly relevant for DC fast charging, where the maximum charging power may be severely limited, even at moderate state of charge levels. First, a reduced order battery circuit model is developed, which retains the nonlinear relationship between state of charge and maximum charging power. Using this model, limits on the battery output voltage and battery charging power are formulated as second-order cone constraints. These constraints are integrated with a linearised power flow model for three-phase unbalanced distribution networks. This provides a new multiperiod optimisation strategy for electric vehicle smart charging. The resulting optimisation is a second-order cone program, and thus can be solved in polynomial time by standard solvers. A receding horizon implementation allows the charging schedule to be updated online, without requiring prior information about when vehicles will arrive

The Value of Reactive Power for Voltage Control in Lossy Networks

Reactive power has been proposed as a method of voltage control for distribution networks, providing a means of increasing the amount of energy transferred from distributed generators to the bulk transmission network. The value of reactive power can therefore be measured according to an increase in transferred energy, where the transferred energy is defined as the total generated energy, less the total network losses. If network losses are ignored, an error in the valuation of a given amount of reactive power will be observed (leading to reactive power provision being under- or over-valued). The non-linear analytic solution of a two-bus network is studied, and non-trivial upper and lower bounds are determined for this `valuation error'. The properties predicted by this two-bus network are demonstrated to hold on a three-phase unbalanced distribution test feeder with good accuracy. This allows for an analytic assessment of the importance of losses in the valuation of reactive power in arbitrary networks

Model Predictive Control for Distributed Microgrid Battery Energy Storage Systems

© 2017 IEEE. This brief proposes a new convex model predictive control (MPC) strategy for dynamic optimal power flow between battery energy storage (ES) systems distributed in an ac microgrid. The proposed control strategy uses a new problem formulation, based on a linear d-q reference frame voltage-current model and linearized power flow approximations. This allows the optimal power flows to be solved as a convex optimization problem, for which fast and robust solvers exist. The proposed method does not assume that real and reactive power flows are decoupled, allowing line losses, voltage constraints, and converter current constraints to be addressed. In addition, nonlinear variations in the charge and discharge efficiencies of lithium ion batteries are analyzed and included in the control strategy. Real-time digital simulations were carried out for an islanded microgrid based on the IEEE 13 bus prototypical feeder, with distributed battery ES systems and intermittent photovoltaic generation. It is shown that the proposed control strategy approaches the performance of a strategy based on nonconvex optimization, while reducing the required computation time by a factor of 1000, making it suitable for a real-time MPC implementation

Residential Load Variability and Diversity at Different Sampling Time and Aggregation Scales

The increasing use of large-scale intermittent distributed renewable energy resources on the electrical power system introduces uncertainties in both network planning and management. In addition to architectural changes to the power system, the applications of demand side response (DSR) also add a dimension of complexity - thereby converting the traditionally passive customers into active prosumers (customers that both produce and consume electricity). It has therefore become important to conduct detailed studies on system load profiles to uncover the nature of the system load. These studies could help distribution network operators (DNOs) to adopt relevant strategies that can accommodate new resources such as distributed generation and energy storage on the evolving distribution network and ensure updated design and management approaches. This paper investigates the relationship between both the system load diversity and variability when different customers are aggregated at different scales. Additionally, the implication of sampling time scales is investigated to capture its effect on load diversity and variability. The study looks at the diversity and variability that is observable from the viewpoint of higher power levels, when interconnecting different sized groupings of customers, at different sampling resolutions. The paper thus concludes that the per-customer capacity requirement of the network decreases as the size of customer groupings increases. The load variability also decreases as the aggregation level increases. For active network management, faster time scales are required at lower aggregation scales due to high load variability

Deep Reinforcement Learning Based Energy Storage Arbitrage With Accurate Lithium-ion Battery Degradation Model

Accurate estimation of battery degradation cost is one of the main barriers for battery participating on the energy arbitrage market. This paper addresses this problem by using a model-free deep reinforcement learning (DRL) method to optimize the battery energy arbitrage considering an accurate battery degradation model. Firstly, the control problem is formulated as a Markov Decision Process (MDP). Then a noisy network based deep reinforcement learning approach is proposed to learn an optimized control policy for storage charging/discharging strategy. To address the uncertainty of electricity price, a hybrid Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) model is adopted to predict the price for the next day. Finally, the proposed approach is tested on the the historical UK wholesale electricity market prices. The results compared with model based Mixed Integer Linear Programming (MILP) have demonstrated the effectiveness and performance of the proposed framework

Decentralised control method for DC microgrids with improved current sharing accuracy

© The Institution of Engineering and Technology 2016. A decentralised control method that deals with current sharing issues in dc microgrids (MGs) is proposed in thisstudy. The proposed method is formulated in terms of 'modified global indicator' concept, which was originally proposedto improve reactive power sharing in ac MGs. In this work, the 'modified global indicator' concept is extended tocoordinate dc MGs, which aims to preserve the main features offered by decentralised control methods such as no need ofcommunication links, central controller or knowledge of the microgrid topology and parameters. This global indicator isinserted between current and voltage variables by adopting a virtual capacitor, which directly produces an output currentsharing performance that is less relied on mismatches of the multi-bus network. Meanwhile, a voltage stabiliser iscomplementary developed to maintain output voltage magnitude at steady state through a shunt virtual resistance. Theoperation under multiple dc-buses is also included in order to enhance the applicability of the proposed controller. Adetailed mathematical model including the effect of network mismatches is derived for analysis of the stability of theproposed controller. The feasibility and effectiveness of the proposed control strategy are validated by simulation andexperimental results

Voltage control loss factors for quantifying DG reactive power control impacts on losses and curtailment

Distributed Generators that use reactive power for voltage control in distribution networks reduce renewable curtailment but can significantly increase network losses, undermining the effectiveness of this control. This paper proposes Voltage Control Loss Factors (VCLFs) as a means of understanding the interactions between reactive power flows, losses and curtailment, focusing on commercial-scale generators in radial systems. The metric uses a substitution-based method, whereby a system with voltage control is compared against a counterfactual with no such control. The proposed method studies this metric by coupling numerically precise black-box simulations with analytic results from a Two-Bus network representation. The latter provides a physical explanation for the numerical simulation results in terms of power, voltage and impedance parameters, providing clear explainability which is absent in traditional approaches for determining distribution loss factors. The whole solution space of the Two-Bus system is explored, and VCLFs are calculated for six cases on three unbalanced test networks to illustrate the approach. Relative losses as high as 30% are found in a system with high branch resistance-reactance ratio and large voltage rise. The results have implications for the design of loss allocation algorithms in distribution networks, and the optimal sizing of power-electronic interfaced Distributed Generators

Taking the long view on short-run marginal emissions: how much carbon does flexibility and energy storage save?

Data availability statement: The data underlying this article is available on request from the corresponding author.Copyright © The Author(s) 2023. Grid-scale electricity storage will play a crucial role in the transition of power systems towards zero carbon. During the transition, investments need to be channeled towards technologies and locations that enable zero carbon operation in the long term, while also delivering security of supply and value for money. We discuss metrics and market signals that are needed to guide this transition towards clean, secure and affordable solutions. Paradoxically, carbon metrics play an important role, but become less effective as a decision tool once the system approaches zero carbon. We critically assess the role of marginal and average emission and question the allocation of marginal emissions in systems where combinations of renewables and storage deliver flexibility. We conclude that, for strategic investments, short-term market signals may not always deliver sufficiently fast or far-sighted outcomes and operational decisions need to consider the merit order of demand as well as supply.UKRI Prospering from the Energy Revolution’s Energy Superhub Oxford demonstrator and ‘Data-driven exploration of the carbon emissions impact of grid energy storage deployment and dispatch’ (DIGEST EP/W027321/1)

Capturing Spatio-Temporal Dependencies in the Probabilistic Forecasting of Distribution Locational Marginal Prices

IEEE This paper presents a new spatio-temporal framework for the day-ahead probabilistic forecasting of Distribution Locational Marginal Prices (DLMPs). The approach relies on a recurrent neural network, whose architecture is enriched by introducing a deep bidirectional variant designed to capture the complex time dynamics in multi-step forecasts. In order to account for nodal price differentiation (arising from grid constraints) within a procedure that is scalable to large distribution systems, nodal DLMPs are predicted individually by a single model guided by a generic representation of the grid. This strategy offers the additional benefit to enable cold-start forecasting for new nodes with no history. Indeed, in case of topological changes, e.g. building of a new home or installation of photovoltaic panels, the forecaster intrinsically leverages the statistical information learned from neighbouring nodes to predict the new DLMP, without needing any modification of the tool. The approach is evaluated, along with several other methods, on a radial low voltage network. Outcomes highlight that relying on a compact model is a key component to boost its generalization capabilities in high-dimensionality, while indicating that the proposed tool is effective for both temporal and spatial learning