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

Optimal Short-term Operation of a Cascaded Hydro-Solar Hybrid System: a Case Study in Kenya

In this paper we propose an optimal dispatch scheme for a cascaded hybrid hydro-solar power system, i.e., a hydroelectric system coupled with solar generation, that maximises the head levels of each dam, and minimises the spillage effects. As a result more water is stored in the dams to meet a given amount of energy providing more flexibility to the system in dry months. This dispatch scheme is based on the development of a simplified hydroelectric power system model which has low computational burden and may be implemented for the short-term operation of a cascaded hydro-solar hybrid power system. To this end, the nonconvex relationships that describe the system physical constraints, e.g., hydroelectric power output, are transformed into affine relationships; thus reducing the computational complexity. The transformations are based on the construction of convex envelopes around bilinear functions, piecewise affine functions, and exploitation of optimisation properties. We validate the proposed framework and quantify the benefits of coupling hydroelectric and solar resources in terms of live water volume in dams and amount of solar a system may withstand with the Tana river cascade located in Kenya through an analysis of incorporating actual system data

Cascade Hydroelectric Power System Model and its Application to an Optimal Dispatch Design

In this paper we propose an optimal dispatch scheme for a cascade hydroelectric power system that maximises the system efficiency, and minimises the spillage effects. Our approach proposes a methodology that has low computational burden and may be implemented for the short-term operation of a cascade hydroelectric power system. To this end, the non- linear relationships that describe the system physical constraints, e.g., power output, are transformed into affine relationships; thus reducing the computational complexity. The transformations are based on the construction of convex envelopes around bilinear functions; piecewise affine functions; and exploitation of optimisation properties. We demonstrate the efficacy of the proposed methodology with the Seven Forks system located in Kenya, and evaluate the performance of our method in terms of water volume and potential energy saved

Clustering of Usage Profiles for Electric Vehicle Behaviour Analysis

Accurately predicting the behaviour of electric vehicles is going to be imperative for network operators. In order for vehicles to participate in either smart charging schemes or providing grid services, their availability and charge requirements must be forecasted. Their relative novelty means that data concerning electric vehicles is scarce and biased, however we have been collecting data on conventional vehicles for many years. This paper uses cluster analysis of travel survey data from the UK to identify typical conventional vehicle usage profiles. To this end, we determine the feature vector, introduce an appropriate distance metric, and choose a number of clusters. Five clusters are identified, and their suitability for electrification is discussed. A smaller data set of electric vehicles is then used to compare the current electric fleet behaviour with the conventional one

Mitigating the Impact of Personal Vehicle Electrification: a Power Generation Perspective

The number of electric vehicles on the road in the UK is expected to rise quickly in the coming years, and this is likely to have an impact on the operation of the power grid. This paper first quantifies the consequences of allowing a completely electric fleet to charge freely, then considers whether there is a practical way in which the impacts can be mitigated. We predict that, with an entirely electric fleet, the UK power generation capacity would need to increase by 1/3. We show that it is possible to completely mitigate this with controlled charging, although substantial infrastructure would be required. However, we propose a simple scheme which could largely avoid the negative effect and does not require the creation of new infrastructure. We show that this reduces the projected increase in peak electricity demand by 80-99%

Robust Optimisation for Hydroelectric System Operation under Uncertainty

In this paper, we propose an optimal dispatch scheme for a cascade hydroelectric power system that maximises the head levels of each dam, and minimises the spillage effects taking into account uncertainty in the net load variations, i.e., the difference between the load and the renewable resources, and inflows to the cascade. By maximising the head levels of each dam the volume of water stored, which is a metric of system resiliency, is maximised. In this regard, the operation of the cascade hydroelectric power system is robust to the variability and intermittency of renewable resources and increases system resilience to variations in climatic conditions. Thus, we demon- strate the benefits of coupling hydroelectric and photovoltaic resources. To this end, we introduce an approximate model for a cascade hydroelectric power system. We then develop correlated probabilistic forecasts for the uncertain output of renewable resources, e.g., solar generation, using historical data based on clustering and Markov chain techniques. We incorporate the gen- erated forecast scenarios in the optimal dispatch of the cascade hydroelectric power system, and define a robust variant of the developed system. However, the robust variant is intractable due to the infinite number of constraints. Using tools from robust optimisation, we reformulate the resulting problem in a tractable form that is amenable to existing numerical tools and show that the computed dispatch is immunised against uncertainty. The efficacy of the proposed approach is demonstrated by means of an actual case study involving the Seven Forks system located in Kenya, which consists of five cascaded hydroelectric power systems. With the case study we demonstrate that the Seven Forks system may be coupled with solar generation since the “price of robustness” is small; thus demonstrating the benefits of coupling hydroelectric systems with solar generatio

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

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

A Novel Modeling Approach to Stochastically Evaluate the Impact of Pore Network Geometry, Chemistry and Topology on Fluid Transport

Fine-grained sandstones, siltstones, and shales have become increasingly important to satisfy the ever-growing global energy demands. Of particular current interest are shale rocks, which are mudstones made up of organic and inorganic constituents of varying pore sizes. These materials exhibit high heterogeneity, low porosity, varying chemical composition and low pore connectivity. Due to the complexity and the importance of such materials, many experimental, theoretical and computational eforts have attempted to quantify the impact of rock features on fuids difusivity and ultimately on permeability. In this study, we introduce a stochastic kinetic Monte Carlo approach developed to simulate fuid transport. The features of this approach allow us to discuss the applicability of 2D vs 3D models for the calculation of transport properties. It is found that a successful model should consider realistic 3D pore networks consisting of pore bodies that communicate via pore throats, which however requires a prohibitive amount of computational resources. To overcome current limitations, we present a rigorous protocol to stochastically generate synthetic 3D pore networks in which pore features can be isolated and varied systematically and individually. These synthetic networks do not correspond to real sample scenarios but are crucial to achieve a systematic evaluation of the pore features on the transport properties. Using this protocol, we quantify the contribution of the pore network’s connectivity, porosity, mineralogy, and pore throat width distribution on the difusivity of supercritical methane. A sensitivity analysis is conducted to rank the signifcance of the various network features on methane difusivity. Connectivity is found to be the most important descriptor, followed by pore throat width distribution and porosity. Based on such insights, recommendations are provided on possible technological approaches to enhance fuid transport through shale rocks and equally complex pore networks. The purpose of this work is to identify the signifcance of various pore network characteristics using a stochastic KMC algorithm to simulate the transport of fuids. Our fndings could be relevant for applications that make use of porous media, ranging from catalysis to radioactive waste management, and from environmental remediation to shale gas production