Cost Efficient Distributed Load Frequency Control in Power Systems

The introduction of new technologies and increased penetration of renewable resources is altering the power distribution landscape which now includes a larger numbers of micro-generators. The centralized strategies currently employed for performing frequency control in a cost efficient way need to be revisited and decentralized to conform with the increase of distributed generation in the grid. In this paper, the use of Multi-Agent and Multi-Objective Reinforcement Learning techniques to train models to perform cost efficient frequency control through decentralized decision making is proposed. More specifically, we cast the frequency control problem as a Markov Decision Process and propose the use of reward composition and action composition multi-objective techniques and compare the results between the two. Reward composition is achieved by increasing the dimensionality of the reward function, while action composition is achieved through linear combination of actions produced by multiple single objective models. The proposed framework is validated through comparing the observed dynamics with the acceptable limits enforced in the industry and the cost optimal setups

Load Frequency Control: A Deep Multi-Agent Reinforcement Learning Approach

The paradigm shift in energy generation towards microgrid-based architectures is changing the landscape of the energy control structure heavily in distribution systems. More specifically, distributed generation is deployed in the network demanding decentralised control mechanisms to ensure reliable power system operations. In this work, a Multi-Agent Reinforcement Learning approach is proposed to deliver an agentbased solution to implement load frequency control without the need of a centralised authority. Multi-Agent Deep Deterministic Policy Gradient is used to approximate the frequency control at the primary and the secondary levels. Each generation unit is represented as an agent that is modelled by a Recurrent Neural Network. Agents learn the optimal way of acting and interacting with the environment to maximise their long term performance and to balance generation and load, thus restoring frequency. In this paper we prove using three test systems, with two, four and eight generators, that our Multi-Agent Reinforcement Learning approach can efficiently be used to perform frequency control in a decentralised way

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

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

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%

Optimal Dispatch of Pumped Storage Hydro Cascade under Uncertainty

In this paper, we propose an optimal dispatch scheme for a pumped storage hydro cascade that maximizes the energy per cubic meter of water in the system taking into account uncertainty in the net load variations. To this end, we introduce a model to describe the behaviour of a pumped storage hydro cascade and formulate its optimal dispatch. We then incorporate forecast scenarios in the optimal dispatch, and define a robust variant of the developed system. The resulting optimization problem is intractable due to the infinite number of constraints. Using tools from robust optimization, 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 a realistic case study based on the Seven Forks system located in Kenya

Effects of Solar and Wind Generation Integration on Feeder Hosting Capacity

With the increased penetration of distributed generation (DG) utilities are beginning to see impacts on their system, especially on the ability of a feeder to accommodate DG. In this paper we introduce a stochastic simulation framework to assess the effects on hosting capacity from solar and wind generation for various loading scenarios. The general approach includes the use of a k-means clustering algorithm for segmenting and grouping the raw wind, solar, and load data to define patterns and assign probabilities to each pattern. Monte Carlo simulations are adopted for calculating probabilistic outcomes for a variety of wind, solar, and load scenarios, with the use of a distribution planning software. The outcomes of the simulations, i.e., statistics of minimum and maximum feeder hosting capacity, are used to derive their probability distribution functions (pdfs). The pdfs of the minimum and maximum hosting capacity provide insights into the effects on loading from various wind and solar DG scenarios. The proposed framework is illustrated for a representative utility feeder

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