Co-optimisation of Planning and Operation forActive Distribution Grids

Given the increased penetration of smart grid technologies, distribution system operators are obliged to consider in their planning stage both the increased uncertainty introduced by non-dispatchable distributed energy resources, as well as the operational flexibility provided by new real-time control schemes. First, in this paper, a planning procedure is proposed which considers both traditional expansion measures, e.g. upgrade of transformers, cables, etc., as well as real-time schemes, such as active and reactive power control of distributed generators, use of battery energy storage systems and flexible loads. At the core of the proposed decision making process lies a tractable iterative AC optimal power flow method. Second, to avoid the need for a real-time centralised coordination scheme (and the associated communication requirements), a local control scheme for the operation of individual distributed energy resources and flexible loads is extracted from offline optimal power flow computations. The performance of the two methods is demonstrated on a radial, low-voltage grid, and compared to a standard local control scheme

A Centralised Control Method for Tackling Unbalances in Active Distribution Grids

Traditional distribution network operators are gradually being transformed to system operators, using modern technologies to ensure a secure and efficient operation in a rapidly changing and uncertain environment. One of their most challenging tasks is to tackle the unbalanced operation of low-voltage networks, traditionally caused by unequal loading and structural asymmetries, and exacerbated by the increased penetration of single-phase distributed energy resources. This paper proposes a centralized operation scheme based on a multi-period optimal power flow algorithm used to compute optimal set-points of the controllable distributed energy resources located in the system. The algorithm reduces the operational cost while satisfying the appropriate security and power quality constraints. Furthermore, the computational tractability of the algorithm and the incremental cost of tackling imbalances in the network are addressed. Finally, the performance of the proposed method is tested on an unbalanced low-voltage distribution network

Optimized Local Control for Active Distribution Grids using Machine Learning Techniques

Modern distribution system operators are facing a changing scenery due to the increasing penetration of distributed energy resources, introducing new challenges to system operation. In order to ensure secure system operation at a low cost, centralized and decentralized operational schemes are used to optimally dispatch these units. This paper proposes a decentralized, real-time, operation scheme for the optimal dispatch of distributed energy resources in the absence of extensive monitoring and communication infrastructure. This scheme uses an offline, centralized, optimal operation algorithm, with historical information, to generate a training dataset consisting of various operating conditions and corresponding distributed energy resources optimal decisions. Then, this dataset is used to design the individual local controllers for each unit with the use of machine learning techniques. The performance of the proposed method is tested on a low-voltage distribution network and is compared against centralized and existing decentralized methods

Optimal planning of distribution grids considering active power curtailment and reactive power control

In this paper, a new planning methodology is proposed for existing distribution grids, considering both passive and active network measures. The method is designed to be tractable for large grids of any type, e.g., meshed or radial. It can be used as a decision-making tool by distribution system operators which need to decide whether to invest in new hardware, such as new lines and transformers, or to initiate control measures influencing the operational costs. In this paper, active power curtailment and reactive power control are taken into account as measures to prevent unacceptable voltage rises as well as element overloads, as these allow postponing network investments. A low-voltage, meshed grid with 27 nodes is used to demonstrate the proposed scheme. In this particular case, the results show that by using control measures, an active distribution system operator can defer investments and operate the existing infrastructure more efficiently. The methodology is able to account for variations in operational and investment costs coming from regulatory influences to provide an insight to the most cost-efficient decision

Data-driven Control Design Schemes in Active Distribution Grids: Capabilities and Challenges

Today, system operators rely on local control of distributed energy resources (DERs), such as photovoltaic units, wind turbines and batteries, to increase operational flexibility. These schemes offer a communication-free, robust, cheap, but rather sub-optimal solution and do not fully exploit the DER capabilities. The operational flexibility of active distribution networks can be greatly enhanced by the optimal control of DERs. However, it usually requires remote monitoring and communication infrastructure, which current distribution networks lack due to the high cost and complexity. In this paper, we investigate data-driven control algorithms that use historical data, advanced off-line optimization techniques, and machine learning methods, to design local controls that emulate the optimal behavior without the use of any communication. We elaborate on the suitability of various schemes based on different local features, we investigate safety challenges arising from data-driven control schemes, and we show the performance of the optimized local controls on a three-phase, unbalanced, low-voltage, distribution network

A Novel ILP Formulation for PCB Maintenance Considering Electrical Measurements and Aging Factors: A “Right to Repair” Approach

The design of longer-lasting products, such as domestic electric appliances, is a key-stone approach of the circular economy to reduce the use of non-reusable materials and the number of wastes to be managed at the end of the product’s life as well as to extend it. The manufacturing of modern electric appliances includes the incorporation of printed circuit boards (PCBs). PCBs provide mechanical support and electrically connect electrical or electronic components using conductive trackpads and other features etched from one or more sheet layers of copper laminated onto and/or between sheet layers of a non-conductive substrate. This paper proposes a PCB maintenance framework, fully compliant with the “Right to Repair” concept, considering the impact of their aging failures based on measurements made on them, as well as the repair and replacement costs of their components. Herein, we present an algorithm that assesses the problem of handling the repair and replacement cost corresponding to specific failures while ensuring that the total cost of repair does not exceed a predefined value. This is achieved through an integer linear programming (ILP) formulation which maximizes the benefit to the life expectancy, Li, of an appliance, constrained by a customer’s limited budget. The proposed methodology is tested with different PCBs and considers different types of appliances. More specifically, two cases concerning PCBs of washing and dishwasher machines are studied to examine the dependency of the solutions on the aging rate of their various components. The simulation results show that considering a medium budget, after 3 years, we can achieve a health benefit of 92.4% for a washing machine’s PCB, while for a dishwasher’s PCB, the health benefit drops to 86.3%