Efficient Computation of Sensitivity Coefficients of Node Voltages and Line Currents in Unbalanced Radial Electrical Distribution Networks

The problem of optimal control of power distribution systems is becoming increasingly compelling due to the progressive penetration of distributed energy resources in this specific layer of the electrical infrastructure. Distribution systems are, indeed, experiencing significant changes in terms of operation philosophies that are often based on optimal control strategies relying on the computation of linearized dependencies between controlled (e.g. voltages, frequency in case of islanding operation) and control variables (e.g. power injections, transformers tap positions). As the implementation of these strategies in real-time controllers imposes stringent time constraints, the derivation of analytical dependency between controlled and control variables becomes a non-trivial task to be solved. With reference to optimal voltage and power flow controls, this paper aims at providing an analytical derivation of node voltage and line current flows as a function of the nodal power injections and transformers tap-changers positions. Compared to other approaches presented in the literature, the one proposed here is based on the use of the [Y] compound matrix of a generic multi-phase radial unbalanced network. In order to estimate the computational benefits of the proposed approach, the relevant improvements are also quantified versus traditional methods. The validation of the proposed method is carried out by using both IEEE 13 and 34 node test feeders. The paper finally shows the use of the proposed method for the problem of optimal voltage control applied to the IEEE 34 node test feeder.Comment: accepted for publication to IEEE Transactions on Smart Gri

Optimal Voltage Regulation of Unbalanced Distribution Networks with Coordination of OLTC and PV Generation

Photovoltaic (PV) smart inverters can regulate voltage in distribution systems by modulating reactive power of PV systems. In this paper, an optimization framework for optimal coordination of reactive power injection of smart inverters and tap operations of voltage regulators for multi-phase unbalanced distribution systems is proposed. Optimization objectives are minimization of voltage deviations and tap operations. A novel linearization method convexifies the problem and speeds up the solution. The proposed method is validated against conventional rule-based autonomous voltage regulation (AVR) on the highly-unbalanced IEEE 37 bus test system. Simulation results show that the proposed method estimates feeder voltage accurately, voltage deviation reductions are significant, over-voltage problems are mitigated, and voltage imbalance is reduced.Comment: IEEE Power and Energy Society General Meeting 201

A Data-driven Approach for Estimating Relative Voltage Sensitivity

Voltage sensitivity expresses analytically the dependency between voltage and active or reactive power. Knowing the voltage sensitivity is necessary in many power system applications, such as the Distributed Energy Resources (DER) optimal placement and control. The majority of voltage sensitivity estimation methods assume having an accurate model of the grid and only consider a balanced grid operation at the nominal point, which is not realistic. In this paper, a method based on Mutual Information (MI) is proposed, which is able to evaluate the nonlinear dependencies between two variables, in order to estimate the relative voltage sensitivity. Contrary to the existing methods, the proposed MI-based approach only requires measurements at the point of interest and does not require any grid model nor measurements from other nodes in the grid. As a use case, the optimal allocation for an Energy Storage System (ESS) in a real medium voltage network in Germany has been presented. Measurement results confirm the effectiveness of the new approach for estimating relative voltage sensitivity

A unified control strategy for active distribution networks via demand response and distributed energy storage systems

AbstractAs part of the transition to a future power grid, distribution systems are undergoing profound changes evolving into Active Distribution Networks (ADNs). The presence of dispersed generation, local storage systems and responsive loads in these systems incurs severe impacts on planning and operational procedures. This paper focuses on the compelling problem of optimal operation and control of ADNs, with particular reference to voltage regulation and lines congestion management. We identify the main challenges and opportunities related to ADNs control and we discuss recent advances in this area. Finally, we describe a broadcast-based unified control algorithm designed to provide ancillary services to the grid by a seamless control of heterogeneous energy resources such as distributed storage systems and demand-responsive loads

Optimal Location and Sizing of Distributed Storage Systems in Active Distribution Networks

Energy balance and ancillary services provided by distributed storage systems to active distribution networks represent two aspects of a single problem that needs to be properly treated in view of the typical distribution networks parameters. In this context, the paper focuses on the problem of the optimal siting and sizing of distributed storage systems. In particular, the paper proposes the formulation of a problem that accounts: (i) the voltage support of storage systems to the grid, (ii) the network losses and (iii) the cost of the energy-flow towards the external grid. As the formulated problem is mixedinteger, non-convex and non-linear, its solution requires the adoption of heuristic techniques. In this respect, a two-stage iterative procedure is proposed. The first stage of the procedure utilizes a genetic algorithm to provide locations and sizes of the distributed storage systems; the second stage evaluates the fitness of the solution provided by the first part by solving a daily AC optimal power flow. An application example, referring to the IEEE 13 busses test feeder, is included in order to demonstrate and discuss the efficiency of the proposed method

Sufficient Conditions for Exact Semidefinite Relaxation of Optimal Power Flow in Unbalanced Multiphase Radial Networks

This paper proves that in an unbalanced multi-phase network with a tree topology, the semidefinite programming relaxation of optimal power flow problems is exact when critical buses are not adjacent to each other. Here a critical bus either contributes directly to the cost function or is where an injection constraint is tight at optimality. Our result generalizes a sufficient condition for exact relaxation in single-phase tree networks to tree networks with arbitrary number of phases.Comment: Accepted by 2019 Conference on Decision and Control (CDC

A Hardware-in-the-Loop test platform for the performance assessment of a PMU-based Real-Time State Estimator for Active Distribution Networks

The paper describes the development of a Hardware- in-the-Loop (HIL) test platform for the performance assessment of a PMU-based sub-second linear Real-Time State Estimator (RTSE) for Active Distribution Networks (ADNs). The estimator relies on the availability of data coming from Phasor Measurement Units (PMUs) and can be applied to both balanced and unbalanced ADNs. The paper first illustrates the architecture of the experimental HIL setup that has been fully designed by the Authors. It consists of a Real-Time Simulator (RTS) that models the electrical network model as well as the measurement infrastructure composed by virtual PMUs. These virtual devices stream their data to a real Phasor Data Concentrator (PDC) suitably coupled with a Discrete Kalman Filter State Estimator (DKF-SE). By using this experimental setup, the paper discusses the performance assessment of the whole process in terms of estimation accuracy and time latencies. In the RTS, a real ADN located in the Netherlands has been modeled together with the associated PMUs

Real-time enforcement of local energy market transactions respecting distribution grid constraints

International audienceFuture electricity distribution grids will host a considerable share of the renewable energy sources needed for enforcing the energy transition. Demand side management mechanisms play a key role in the integration of such renewable energy resources by exploiting the flexibility of elastic loads, generation or electricity storage technologies. In particular, local energy markets enable households to exchange energy with each other while increasing the amount of renewable energy that is consumed locally. Nevertheless, as most ex-ante mechanisms, local market schedules rely on hour-ahead forecasts whose accuracy may be low. In this paper we cope with forecast errors by proposing a game theory approach to model the interactions among prosumers and distribution system operators for the control of electricity flows in real-time. The presented game has an aggregative equilibrium which can be attained in a semi-distributed manner, driving prosumers towards a final exchange of energy with the grid that benefits both households and operators, favoring the enforcement of prosumers' local market commitments while respecting the constraints defined by the operator. The proposed mechanism requires only one-to-all broadcast of price signals, which do not depend either on the amount of players or their local objective function and constraints, making the approach highly scalable. Its impact on distribution grid quality of supply was evaluated through load flow analysis and realistic load profiles, demonstrating the capacity of the mechanism ensure that voltage deviation and thermal limit constraints are respected

Aggregation of Power Capabilities of Heterogeneous Resources for Real-Time Control of Power Grids

Aggregation of electric resources is a fundamental function for the operation of power grids at different time scales. In the context of a recently proposed framework for the real-time control of microgrids with explicit power setpoints, we define and formally specify an aggregation method that explicitly accounts for delays and message asynchronism. The method allows to abstract the details of resources using high-level concepts that are device and grid-independent. We demonstrate the application of the method to a Cigre benchmark with heterogenous and lowinertia resources