Electric Distribution Network Management and Control

This book chapter explores existing and emerging flexibility options that can facilitate the integration of large-scale variable renewable energy sources (vRESs) in next-gen electric distribution networks while minimizing their side-effects and associated risks. Nowadays, it is widely accepted that integrating vRESs is highly needed to solve a multitude of global concerns such as meeting an increasing demand for electricity, enhancing energy security, reducing heavy dependence on fossil fuels for energy production and the overall carbon footprint of power production. As a result, the scale of vRES development has been steadily increasing in many electric distribution networks. The favorable agreements of states to curb greenhouse gas emissions and mitigate climate change, along with other technical, socio-economic and structural factors, is expected to further accelerate the integration of renewables in electric distribution networks. Many states are now embarking on ambitious “clean” energy development targets. Distributed generations (DGs) are especially attracting a lot of attention nowadays, and planners and policy makers seem to favor more on a distributed power generation to meet the increasing demand for electricity in the future. And, the role of traditionally centralized power production regime is expected to slowly diminish in future grids. This means that existing electric distribution networks should be readied to effectively handle the increasing penetration of DGs, vRESs in particular, because such systems are not principally designed for this purpose. It is because of all this that regulators often set a maximum RES penetration limit (often in the order of 20%) which is one of the main factors that impede further development of the much-needed vRESs.

The main challenge is posed by the high-level variability as well as partial unpredictability of vRESs which, along with traditional sources of uncertainty, leads to several technical problems and increases operational risk in the system. This is further exacerbated by the increased uncertainty posed by the continuously changing and new forms of energy consumption such as power-to-X and electric vehicles. All these make operation and planning of distribution networks more intricate. Therefore, there is a growing need to transform existing systems so that they are equipped with adequate flexibility mechanisms (options) that are capable of alleviating the aforementioned challenges and effectively managing inherent technical risk. To this end, the main focus of this chapter is on the optimal management of distribution networks featuring such flexibility options and vRESs. This analysis is supported by numerical results from a standard network system. For this, a reasonably accurate mathematical optimization model is developed, which is based on a linearized AC network model. The results and analysis in this book chapter have policy implications that are important to optimally design ad operate future grids, featuring large-scale variable energy resources. In general, based on the analysis results, distribution networks can go 100% renewable if various flexibility options are adequately deployed and operated in a more efficient manner.

This chapter first examines balanced and unbalanced electric distribution network modelling techniques and then tests their effects on power flow analysis with practical electric distribution networks. Test results show that distribution network operating conditions obtained from the balanced and unbalanced model-based power flows may have obvious difference in terms of voltage violations and overloaded equipment. These results demonstrate the importance of adopting unbalanced network modelling in improving the accuracy of power flow. Furthermore, this chapter presents an unbalanced model-based DMS application in loss reduction, an essential application for the energy efficiency improvement in electric distribution networks. The loss reduction often involves the control of reactive power (VAR) resources to optimize the VAR flow in the distribution network. Based on the unbalanced power flow model, an advanced loss reduction approach is developed to achieve the optimal control coordination among multiple capacitors and distributed energy resources such as solar and wind resources. The effectiveness of the presented approach is demonstrated on practical utility distribution networks with varying degree of unbalance and model complexity.

This chapter presents some advanced tools for low voltage (LV) network demand simulation. Such methods will be required to help distribution network operators (DNOs) cope with the increased uptake of low carbon technologies and localised sources of generation. This will enable DNOs to manage the current network, simulate the effect of various scenarios and run load flow analysis. In order to implement such analysis requires high resolution smart meter data for the various customers connected to the network. However, only small amounts of individual smart meter data will be available and such data could be expensive. In likelihood, smart meter data is only going to be freely available at the aggregate level. Hence, in general, to implement LV network tools, customer loads will need to be simulated based on the assumption of limited amounts of monitored data. In addition, due to the high volatility of LV electric distribution networks, demand uncertainty must also be captured within a simulation tool. In this chapter, a number of methods are described for simulating demand on low voltage feeders which rely only on relatively small samples of smart meter data and monitoring. Firstly, a method called ‘buddying’ is described for assigning realistic profiles to unmonitored customers by buddying them to a customer who is monitored. Secondly, a number of methods are presented for capturing the uncertainty on the network. Finally the uncertainty models are incorporated into the buddying method and implemented in a load flow analysis tool on a number of real feeders. Both the buddying and the uncertainty estimation are presented for two different cases based on whether LV substation monitoring is present or not. This illustrates the different impacts of monitoring availability on the modelling tools. This chapter demonstrates the presented methods on a large range of real LV feeders.

With increasing connection of distributed generations (DGs), the power flow in electric distribution network is no longer unidirectional and the network has become active distribution network. Thus, the connection of DGs in electric distribution networks has created a challenge for distribution network operators due to bidirectional power flow. One of the main technical challenges of an active distribution network is to maintain an acceptable voltage level in the system. This has initiated efforts in using various voltage control strategies to regulate the network voltage profile so that the voltage is maintained at its allowable voltage limits. A number of voltage control methods have been applied to solve voltage control problems associated with the connection of DGs in a distribution system. These voltage control strategies may be classified as decentralized or distributed and centralized or coordinated voltage control. From the literature, a number of coordinated voltage control strategies have been implemented to provide better and faster control to the system. This chapter presents an improved coordinated voltage control method in active electric distribution network by coordinating the three voltage control methods, namely, power factor control, on load tap changer control and generation curtailment control. These voltage control methods are coordinated using fuzzy logic by considering the load bus voltages and DG power as inputs and the voltage control actions as the outputs. The fuzzy logic if-then control rules which are generated to build the fuzzy logic control system are based on the simulations results as well as from the previous works. Results obtained using the fuzzy logic based coordinated voltage control has shown that the voltages are able to be kept within its permissible limits.

This chapter reviews promising concepts for distribution network oriented demand response. Current demand response (DR) programs are designed for wholesale markets and utility level issues, neglecting local challenges that distribution network operators (DNOs) face in daily operation. Deployment of DR to specific parts of distribution networks can enable additional services and benefits. The literature hosts promising concepts and methods that gain popularity. However, there is a number of conflicting cases that require particular consideration. This chapter presents insight into use of DR in distribution network planning and operation with special focus on promising service opportunities, developing concepts and integration of local DR programs with utility-driven DR programs.

This chapter discusses the feasibility of using customer coupon demand response in meshed secondary networks. Customers are rewarded by coupons to achieve the objective of optimal operation cost during peak periods. The interdependence of the locational marginal price and the demand is modeled by an artificial neural network. The effect of multiple load aggregators participating in customer coupon demand response is also investigated. Because load aggregators satisfy different proportions of the objective, a fairness function is defined that guarantees that aggregators are rewarded in correspondence with their participation towards the objective. Energy loss is also considered in the objective as it is an essential part of the electric distribution networks. A dynamic coupon mechanism is designed to cope with the changing nature of the demand. To validate the effectiveness of the method, simulations of the presented method have been performed on a real heavily-meshed distribution network in this chapter. The results show that customer coupon demand response significantly contributes to shaving the peak, therefore, bringing considerable economic savings and reduction of loss.

This chapter presents a transactive approach to the optimal scheduling for prosumers in coupled electric and natural gas distribution networks, to help the integration of various distributed energy resources (DERs). DERs are co-ordinately operated in the form of a virtual power plant (VPP), which actively participates in the day-ahead and real-time electricity markets, as well as the wholesale gas market. In the day-ahead (DA) electricity and wholesale gas markets, a VPP aims to maximize expected profits by determining the unit commitments and hourly scheduling of DERs. In the real-time (RT) balancing market, a VPP adjusts DER schedules to minimize imbalance costs. This chapter addresses the energy conversions between electric power and gas loads and investigates the interacting operations of electric and gas distribution networks. The simulation results show that hierarchical, coordinated power and gas scheduling can identify more accurate operation plans for coupled transactive energy networks.

This chapter presents the optimal switch deployment in distribution systems. First, an explanation regarding different types of switches and their functionality is introduced. Then, a fundamental description of fault management procedure in distribution networks is presented. Thereafter, the mathematical formulation of optimal fault management process is described. Optimal switch deployment problem is formulated in the format of mixed integer programming (MIP). The impact of remote controlled switch (RCS) and manual switch (MS) is scrutinized on the interruption cost once they are installed either individually or simultaneously. The concept of switch malfunctions is explained and the influence of this issue on the optimal solution of the problem is discussed. Finally, the effect of uncertain parameters such as failure rate and repair time on the solution of switch deployment problem is investigated. It was shown that the uncertainty imposes a significant risk on distribution companies (DisCos).

This chapter introduces a multi-time scale model predictive control (MPC) approach which is stochastically applied in the cooperative distributed energy scheduling problem of the microgrids (MG). The cooperative distributed approach is preferred, since a centralized one is not applicable in a competitive power market environment because it requires all the data of all the MGs, which is impractical. In this chapter, in order to deal with the variability and uncertainties associated with output power of the renewable energy resources (RES) and load demand, stochastic MPC is applied in distributed energy scheduling problem of MGs. Additionally, considering multi-time scale approach in the stochastic MPC is capable of simultaneously having vast vision for the optimization time horizon and precise resolution for the problem variables. Herein, each MG with a different set of sources is able to transact power with the electricity market and the neighboring MGs. The numerical study demonstrates that cooperation of the MGs in the distributed energy scheduling problem is beneficial, and also the multi-time scale MPC is advantageous compared to the single-time scale MPC in both non-cooperative and cooperative distributed energy scheduling problems.

This chapter presents methodologies to assess the impact of distribution generation on electric distribution network protection systems for an integrated network planning. The distributed generation (DG) alternative is seen as a shift in the paradigm of energy generation in the world. The adoption of renewable sources of energy for residential or commercial production brings not only environmental benefits, but also an opportunity to ease the supplying difficulties found in many countries. Despite solving some of the energy suppling problems, this paradigm change caused by the insertion of DG in electric distribution networks can bring some undesired technical impacts. The typical impacts assessed in the electric distribution networks planning involve the expansion of the network, such as losses, power factor, line loading, and voltage profiles, among others. However, a massive insertion of DG may also cause significant problems to the network protection, which involves the protection planning. In this way, it is necessary to identify potential protection issues and design means to model, diagnose and mitigate such issues. This chapter aims at describing the importance of predicting the potential impacts of high penetration of renewable sources on the protection system from electric distribution networks, in order to achieve an integrated network planning. Thus, in the beginning of this chapter it is discussed the main protection system issues that may arise from the high penetration of distributed generation, such as loss of protection coordination, overvoltage, loss of protection sensitivity, directional false tripping, unwanted fuse blowing, beside others. The chapter than demonstrates possible models utilized to assess those impacts, such as network modeling and renewable sources modeling, possible approaches such as the probabilistic and deterministic perspectives, regarding DG allocation algorithms and the possible methodologies for assessment such as scenarios or sensitivity analysis. The methodologies evaluate the protection impacts and are based on several short circuit calculations and for the probabilistic approach, the Monte Carlo Method. The results shown in the chapter may represent the calculation of such impacts for electric distribution networks. Those may contain loss of sensitivity, directional false tripping and unwanted fuse blowing impact calculations for some networks to illustrate the methodologies. To conclude the chapter, there will be more discussions regarding the results presented and the potential benefits of including this analysis on the integrated distribution network planning. In the end, the chapter illustrates the application of the presented methodologies with a case study using a real distribution network.

This chapter presents modeling of generators that are used in wind farms such as squirrel cage induction generators (SCIG), doubly fed induction generators (DFIG) and, permanent magnet synchronous generator (PMSG). Installing wind farms must fulfill some rules or requirements. These requirements are developed by transmission system operator in order to guarantee the continuity and stability of the interconnected grid. This chapter presents the stability of two different types of combined wind farms. The first type is based on a combination of SCIG and DFIG wind turbines and known as combined wind farm (CWF). CWF collects the benefits of SCIG and DFIG where SCIG is cheaper compared with DFIG and PMSG. Despite DFIG is expensive, DFIG is more stable than SCIG. CWF is more suitable for developing countries. The second type is based on a combination of modern generators DFIG and PMSG and known as modern combined wind farm (MCWF). MCWF collects the benefits of DFIG and PMSG where DFIG features by its ability to control the active power independently of reactive power while PMSG can operate used for small and medium powers. This chapter discusses the impact of CWF and MCWF on the stability of interconnected electric distribution networks during single line to ground and double lines fault as examples of unsymmetrical and during three phase fault and three phase open circuit fault as examples for symmetrical. Also, this chapter discusses the impact of CWF and MCWF on the stability of interconnected electric distribution networks during different types of operation conditions of electric distribution networks such as voltage sage and over voltage.

The advanced metering infrastructure (AMI) has been recognized as a key communication mechanism in the modern distribution grid. As a result, integrating AMI with distribution management system (DMS) has become the focal point of distribution utilities during the past several years with the objective of enabling new applications and enhancing existing ones. In addition, with influx of massive real-time and near real-time measurements, speed up electric distribution network applications using graphic processing unit (GPU) technologies becomes attractive. Hence, the purpose of this chapter is two-fold: First it reviews a unified integration solution that enables DMS systems to flexibly adapt to various AMI systems with different communication protocols and meter data models. The feasibility and effectiveness of the integration solution are demonstrated through practical test scenarios. Second, it discusses GPU technologies and explores their applications in terms of state estimation and power flow computations. It concludes that GPU has significant potentials in improving the performance of distribution network applications. However, to unleash its power, the applications in distribution network need to be re-architected toward a GPU friendly architecture.