Climate change has brought about legally-binding targets for Scotland, the U.K. and the E.U. to reduce greenhouse gas emissions and source a share of overall energy consumption from renewable energy resources by 2020. With severe limitations in the transport and heating sectors the onus is on the electricity sector to provide a significant reduction in greenhouse gas emissions and introduce a substantial increase in renewable energy production. The most attractive renewable energy resources are located in the geographic extremes of the country, far from the large population densities and high voltage, high capacity transmission networks. This means that the majority of renewable generation technologies will need to connect to the conventionally passive, lower voltage distribution networks. The integration of Distributed Generation (DG) is severely restricted by the technical limitations of the passively managed lower voltage infrastructure. Long lead times and the capital expenditure of traditional electricity network reinforcement can significantly delay or make the economics of some renewable generation schemes unviable. To be able to quickly and cost-effectively integrate significant levels of DG, the conventional fit-and-forget approach will have to be evolved into a ‘connect-and-manage’ system using active network management (ANM) techniques. ANM considers the real-time variation in generation and demand levels and schedules electricity network control settings to alleviate system constraints and increase connectable capacity of DG. This thesis explores the extent to which real time adjustments to DG and network asset controller set-points could allow existing networks to accommodate more DG. This thesis investigates the use of a full AC OPF technique to operate and schedule in real time variables of ANM control in distribution networks. These include; DG real and reactive power output and on-load-tap-changing transformer set-points. New formulations of the full AC OPF problem including multi-objective functions, penalising unnecessary deviation of variable control settings, and a Receding-Horizon formulation are assessed. This thesis also presents a methodology and modelling environment to explore the new and innovative formulations of OPF and to assess the interactions of various control practices in real time. Continuous time sequential, single scenario, OPF analyses at a very short control cycle can lead to the discontinuous and unnecessary switching of network control set-points, particularly during the less onerous network operating conditions. Furthermore, residual current flow and voltage variation can also gave rise to undesirable network effects including over and under voltage excursion and thermal overloading of network components. For the majority of instances, the magnitude of constraint violation was not significant but the levels of occurrence gave occasional cause for concern. The new formulations of the OPF problem were successful in deterring any extreme and unsatisfactory effects. Results have shown significant improvements in the energy yield from non-firm renewable energy resources. Initial testing of the real time OPF techniques in a simple demonstration network where voltage rise restricted the headroom for installed DG capacity and energy yield, showed that the energy yield for a single DG increased by 200% from the fit-and-forget scenario. Extrapolation of the OPF technique to a network with multiple DGs from different types of renewable energy resources showed an increase of 216% from the fit-and-forget energy yield. In a much larger network case study, where thermal loading limits constrained further DG capacity and energy yield, the increase in energy yield was more modest with an average increase of 45% over the fit-and-forget approach. In the large network where thermal overloading prevailed there was no immediate alternative to real power curtailment. This work has demonstrated that the proposed ANM OPF schemes can provide an intelligent, more cost effective and quicker alternative to network upgrades. As a result, DNOs can have a better knowledge and understanding of the capabilities and technical limitations of their networks to absorb DG safely and securely, without the expense of conventional network reinforcement.

In distribution networks, the traditional way to eliminate network stresses caused by increasing generation and demand is to reinforce the primary network assets. A cheaper alternative is active network management (ANM) which refers to real-time network control to resolve power flow, voltage, fault current and security issues. However, there are two limitations in ANM. First, previous ANM strategies investigated generation side and demand side management separately. The generation side management evaluates the value from ANM in terms of economic generation curtailment. It does not consider the potential benefits from integrating demand side response such as economically shifting flexible load over time. Second, enhancing generation side management with load shifting requires the prediction of network stress whose accuracy will decrease as the lead time increases. The uncertain prediction implies the potential failure of reaching expected operational benefits. However, there is very limited investigation into the trade-offs between operational benefit and its potential risk. In order to tackle the challenges, there are two aspects of research work in this thesis. 1) Enhanced ANM. It proposes the use of electric vehicles (EVs) as responsive demand to complement generation curtailment strategies in relieving network stress. This is achieved by shifting flexible EV charging demand over time to absorb excessive wind generation when they cannot be exported to the supply network. 2) Uncertainty management. It adopts Sharpe Ratio and Risk Adjust Return On Capital concepts from financial risk management to help the enhanced ANM make operational decisions when both operational benefit and its associated risk are considered. Copula theory is applied to further integrate correlations of forecasting errors between nodal power injections (caused by wind and load forecasting) into uncertainty management. The enhanced ANM can further improve network efficiency of the existing distribution networks to accommodate increasing renewable generation. The cost-benefit assessment informs distribution network operators of the trade-off between investment in ANM strategy and in the primary network assets, thus helping them to make cost-effective investment decisions. The uncertainty management allows the impact of risks that arise from network stress prediction on the expected operational benefits to be properly assessed, thus extending the traditional deterministic cost-benefit assessment to cost-benefit-risk assessment. Moreover, it is scalable to other systems in any size with low computational burden, which is the major contribution of this thesis.

Voltage control in distribution networks is becoming more challenging due to the growing amount of distributed generation that is being connected to the distribution networks in addition to increasing load. The output of the distributed generation can radically change power flows and voltage profiles in distribution networks, creating conditions that adversely affect the performance of automatic voltage control schemes and in addition cause unacceptable voltage rise. On the other hand, inherent limitations and current operational policies of AVC schemes very often restrict the output of DG or even prevent its connection. This thesis investigates and analyses voltage control in terms of the shift from passive to active distribution networks. The thesis also reviews the performance of AVC schemes under varying load and generation output conditions, investigates effective utilisation of distribution network assets and methods to accommodate active voltage control schemes into existing infrastructure. A range of active voltage control and management schemes based on coordinated voltage control is presented and assessed. These schemes can be used to improve the voltage profile in distribution networks and increase their ability to accommodate distributed generation. The functionality of each scheme is assessed based on a number of factors such as the ability of the scheme to increase network capacity, reliability and accuracy. Simulation software to accurately evaluate the performance of an active voltage control scheme in a particular distribution network scenario is essential before the scheme can be deployed. Formal assessment of advanced AVC models and SuperTAPP n+ functionality is performed using simulation software as developed and presented in this thesis. The accuracy of the software results and performance of the SuperTAPP n+ scheme is validated based on network trials carried out in EDF Energy Networks.

The electricity network is undergoing significant changes in the transition to a low carbon system. The growth of renewable distributed generation (DG) creates a number of technical and economic challenges in the electricity network. While the development of the smart grid promises alternative ways to manage network constraints, their impact on the ability of the network to accommodate DG – the ‘hosting capacity’- is not fully understood. It is of significance for both DNOs and DGs developers to quantify the hosting capacity according to given technical or commercial objectives while subject to a set of predefined limits. The combinational nature of the hosting capacity problem, together with the intermittent nature of renewable generation and the complex actions of smart control systems, means evaluation of hosting capacity requires appropriate optimisation techniques. This thesis extends the knowledge of hosting capacity. Three specific but related areas are examined to fill the gaps identified in existing knowledge. New evaluation methods are developed that allow the study of hosting capacity (1) under different curtailment priority rules, (2) with harmonic distortion limits, and (3) alongside energy storage systems. These works together improve DG planning in two directions: demonstrating the benefit provided by a range of smart grid solutions; and evaluating extensive impacts to ensure compliance with all relevant planning standards and grid codes. As an outcome, the methods developed can help both DNOs and DG developers make sound and practical decisions, facilitating the integration of renewable DG in a more cost-effective way.

Renewable energy generation is becoming a major part of energy supply, often in the form of distributed generation (DG) connected to distribution networks. While growth has been rapid, there is awareness that limitations on spare capacity within distribution (and transmission) networks is holding back development. Developments are being shelved until new network reinforcements can be built, which may make some projects non-viable. Reinforcements are costly and often underutilised, typically only loaded to their limits for a few occasions during the year. In order to accommodate new DG without the high costs or delays, active network management (ANM) is being promoted in which generation and other network assets are controlled within the limits of the existing network. There is a great deal of complexity and uncertainty associated with developing ANM and devising coherent plans to accommodate new DG is challenging for Distribution Network Operators (DNOs). As such, there is a need for robust network planning tools that can explicitly handle ANM and which can be trusted and implemented easily. This thesis describes the need for and the development of a new distribution expansion planning framework that provides DNOs with a better understanding of the impacts created by renewable DG and the value of ANM. This revolves around a heuristic planning framework which schedules necessary upgrades in power lines and transformers associated with changes in demand as well as those driven by the connection of DG. Within this framework a form of decentralised, adaptive control of DG output has been introduced to allow estimation of the impact of managing voltage and power flow constraints on the timing and need for network upgrades. The framework is initially deployed using simple scenarios but a further advance is the explicit use of time series to provide substantially improved estimates of the levels of curtailment implied by ANM. In addition, a simplified approach to incorporating demand side management has been deployed to facilitate understanding of the scope and role this may play in facilitating DG connections.

Transition towards low-carbon energy systems requires an increase in the volume of renewable Distributed Generation (DG), particularly wind and photovoltaic, connected to distribution networks. To facilitate the connection of renewable DG without the need for expensive and time-consuming network reinforcements, distribution networks should move from passive to active methods of operation, whereby technical network constraints are actively managed in real time. This requires the deployment of control solutions that manage network constraints and, crucially, ensure adequate levels of energy curtailment from DG plants by using other controllable elements to solve network issues rather than resorting to generation curtailment only. This thesis proposes a deterministic distribution Network Management System (NMS) to facilitate the connections of renewable DG plants (specifically wind) by actively managing network voltages and congestion in real time through the optimal control of on-load tap changers (OLTCs), DG power factor and, then, generation curtailment as a last resort. The set points for the controllable elements are found using an AC Optimal Power Flow (OPF). The proposed NMS considers the realistic modelling of control by adopting one-minute resolution time-series data. To decrease the volumes of control actions from DG plants and OLTCs, the proposed approach departs from multi-second control cycles to multi-minute control cycles. To achieve this, the decision-making algorithm is further improved into a risk-based one to handle the uncertainties in wind power throughout the multi-minute control cycles. The performance of the deterministic and the risk-based NMS are compared using a 33 kV UK distribution network for different control cycles. The results show that the risk-based approach can effectively manage network constraints better than the deterministic approach, particularly for multi-minute control cycles, reducing also the number of control actions but at the expense of higher levels of curtailment. This thesis also proposes energy storage sizing framework to find the minimum power rating and energy capacity of multiple storage facilities to reduce curtailment from DG plants. A two-stage iterative process is adopted in this framework. The first stage uses a multi-period AC OPF across the studied horizon to obtain initial storage sizes considering hourly wind and load profiles. The second stage adopts a high granularity minute-by-minute control driven by a mono-period bi-level AC OPF to tune the first-stage storage sizes according to the actual curtailment. The application of the proposed planning framework to a 33 kV UK distribution network demonstrates the importance of embedding real-time control aspects into the planning framework so as to accurately size storage facilities. By using reactive power capabilities of storage facilities it is possible to reduce storage sizes. The combined active management of OLTCs and power factor of DG plants resulted in the most significant benefits in terms of the required storage sizes.

Yes / This paper proposes a novel method for planning active distribution networks (ADNs) with the integration of an active network management (ANM) scheme using coordinated voltage control (CVC) through on-load tap changer (OLTC) transformers. The method was formulated as a security-constrained optimal power flow (SCOPF) problem to minimize total operational costs, which maximizes the utilization of renewable distributed generators (DGs) over a planning horizon. The ANM scheme was applied using OLTC to ensure safe operation and reduce voltage violations in the network. To analyse the impact of ANM, the planning problem was examined both with and without the ANM scheme. Moreover, SCOPF, considering the N-1 line contingency analysis and multi-DG configuration, was implemented to analyse the feasibility of the proposed method and the advantages of ANM under contingency situations. The method was validated on a weakly-meshed 16-bus UK generic distribution system (UKGDS). The results showed that ANM can lower operational costs and maintain network voltage for operation in feasible conditions even in the case of a contingency. Moreover, the ANM scheme mitigated the voltage rise effect caused by DGs and maximized their utilization.

Yes / In this paper, a stochastic method for active distribution networks planning within a distribution market environment considering multi-configuration of wind turbines is proposed. Multi-configuration multi-scenario market-based optimal power flow is used to maximize the social welfare considering uncertainties related to wind speed and load demand and different operational status of wind turbines (multiple-wind turbine configurations). Scenario-based approach is used to model the abovementioned uncertainties. The method evaluates the impact of multiple-wind turbine configurations and active network management schemes on the amount of wind power that can be injected into the grid, the distribution locational marginal prices throughout the network and on the social welfare. The effectiveness of the proposed method is demonstrated with 16-bus UK generic distribution system. It was shown that multi-wind turbine configurations under active network management schemes, including coordinated voltage control and adaptive power factor control, can increase the amount of wind power that can be injected into the grid; therefore, the distribution locational marginal prices reduce throughout the network significantly.

Yes / In this paper, a novel deterministic approach for the planning of active distribution networks within a distribution market environment considering multi-configuration of wind turbines (WTs) and photovoltaic (PV) cells is proposed. Multi-configuration multi-period market-based optimal power flow is utilized for maximizing social welfare taking into account uncertainties associated with wind speed, solar irradiance and load demand as well as different operational status of WTs and PVs. Multi-period scenarios method is exploited to model the aforementioned uncertainties. The proposed approach assesses the effect of multiple-configuration of PVs and WTs on the amount of wind and solar power that can be produced, the distribution locational marginal prices all over the network and on the social welfare. The application of the proposed approach is examined on a 30-bus radial distribution network. / This work was supported in part by the Royal Academy of Engineering Distinguished Visiting Fellowship Grant DVF1617/6/45 and by the University of Bradford, UK under the CCIP grant 66052/000000.

No / This chapter proposes a novel method for the operation of active distribution networks within a distribution market environment taking into account multi-configuration of wind turbines. Multi-configuration multi-scenario market-based optimal power flow is used to maximise the social welfare considering uncertainties related to wind speed and load demand. Scenario based approach is used to model the uncertainties. The method assesses the impact of multiple-wind turbine configurations on the amount of wind power that can be injected into the grid and the distribution locational marginal prices throughout the network. The effectiveness of the proposed method is demonstrated with 16-bus UK generic distribution system.