This paper introduces a scalable framework to coordinate the net load scheduling, sharing, and matching in a neighborhood of residential prosumers connected to the grid. As the prosumers are equipped with smart appliances, photovoltaic panels, and battery energy storage systems, they take advantage of their consumption, generation, and storage flexibilities to exchange energy with neighboring prosumers through negotiating on the amount of energy and its price with an aggregator. The proposed framework comprises two separate multi-objective mixed integer nonlinear programming optimization models for prosumers and the aggregator. Prosumers' objective is to maximize the comfort level and minimize the electricity cost at each instant of time, while aggregator intends to maximize its profit and minimize the grid burden by matching prosumers' supply and demand. The evolutionary Non-dominated Sorting Genetic Algorithm-III (NSGA-III) is employed to generate a set of feasible non-dominated solutions to the optimization problem of each individual prosumer and the aggregator. As a bilateral negotiation between each prosumer and the aggregator results in significant computational and communication overhead, a virtual power plant is introduced as an intermediator on behalf of all prosumers to proceed the negotiation with the aggregator in a privacy-preserving non-cooperative environment, where no private information is shared. Hence, an automated negotiation approach is embedded in the framework, which enables the negotiators to reactively negotiate on concurrent power and price using private utility functions and preferences. To converge to an acceptable agreement, the negotiation approach follows an alternating-offer production protocol and a reactive utility value concession strategy. The effectiveness of the framework is evaluated by several economic and environmental assessment metrics through a variety of numerical simulations.

A new method for the control of a battery energy storage system and its implementation on a 25 kW solar system to compensate solar power intermittency and improve distribution grid power quality is presented in this paper. The novelty of the proposed method is to provide a systematic way to optimize the size of the battery capacity for the desired level of solar power smoothing. This goal is achieved by designing a two-stage filter solution. The first stage is a fast response digital finite impulse response (FIR) filter that makes a trade-off between smoothing of the solar output and battery capacity. This paper proposes an optimal design of a minimum-length, low-group-delay FIR filter by employing convex optimization, discrete signal processing, and polynomial stabilization techniques. The new strategy proposed in this paper formulates the design of a length-N low-group-delay FIR filter as a convex second-order cone programming, which guarantees that all the filter zeros are inside the unit circle (minimum-phase). A quasi-convex optimization problem is formulated to minimize the length of the low-group-delay FIR filter. The second-stage filter is designed to level the battery charging load. The effectiveness and performance of the proposed approach is demonstrated by simulation results and also over a real-case implementation.

This paper proposes and implements a model-free optimal strategy to regulate the voltage in distribution systems by effective control of Inverter-interfaced Distributed Energy Resources (I-DERs) output power. In distribution systems, where the ratio of R/X is considerable, reactive power control is not always an optimum solution for voltage regulation. With the aim of improving voltage regulation, this paper proposes an alternative method to optimally coordinate active and reactive power of I-DERs in the grid. The proposed method does not need to have a system model and phase measurement, which are barriers in control and operation of behind-the-meter assets. The paper formulates the problem of voltage regulation in form of equilibrium-map-with-optimum, without any restriction on system configuration, amount of reverse power flow and losses. Using the extremum seeking control technique and without an explicit model of distribution circuit, the ratio of active and reactive power is coordinated in real time. The amount of exchanged apparent power is also adjusted simultaneously. Simulation results for a typical distribution circuit verify the effectiveness of the proposed approach in improving the voltage regulation performance. The experimental results also show a 20 percent reduction in use of system capacity to provide the desired voltage regulation performance.

In this paper, super-short-term prediction of solar power generation for applications in dynamic control of energy system has been investigated. In order to follow and satisfy the dynamics of the controller, the deployed prediction method should have a fast response time. To this end, this paper proposes fast prediction methods to provide the control system with one step ahead of solar power generation. The proposed methods are based on univariate time series prediction. That is, instead of using external data such as the weather forecast as the input of prediction algorithms, they solely rely on past values of solar power data, hence lowering the volume and acquisition time of input data. In addition, the selected algorithms are able to generate the forecast output in less than a second. The proposed methods in this paper are grounded on four well-known prediction algorithms including Autoregressive Integrated Moving Average (ARIMA), K-Nearest Neighbors (kNN), Support Vector Regression (SVR), and Random Forest (RF). The speed and accuracy of the proposed algorithms have been compared based on two different error measures, Mean Absolute Error (MAE) and Symmetric Mean Absolute Percentage Error (SMAPE). Real world data collected from the PV installation at the University of California, Riverside (UCR) are used for prediction purposes. The results show that kNN and RF have better predicting performance with respect to SMAPE and MAE criteria.

The recent advent of distribution-level phasor measurement units (D-PMUs), a.k.a., micro-PMUs, has introduced a wide range of new applications in power distribution systems. A sub-class of such emerging applications are called event-based methods. These methods focus on the analysis of events in the stream of micro-PMU measurements to achieve situational awareness, enhance load modeling, integrate distributed energy resources, etc. In this paper, we explore a scenario, where a cyberattack compromises the micro-PMU measurements during an event. Such a targeted attack could be limited in scope but result in a major impact on the operation of the power grid by highly deviating the outcome of the event-based methods. First, we investigate and model two types of such attacks, event-unsynchronized (basic) attacks and event-synchronized (advanced) attacks. We then conduct a geometric analysis to understand each attack type, in a setting where the events are represented in the phasor domain in a differential mode. Next, we introduce a novel method to detect the presence of the attack and then identify which micro-PMUs are compromised so as to discard the compromised measurements as a defense mechanism. The proposed approach makes critical use of magnitude as well as phase angle measurements from micro-PMUs. The method is tested on the IEEE 33-bus power distribution test system.

This article proposes a reactive power control technique to regulate voltage profiles in low voltage (LV) distribution networks with high penetration of photovoltaic (PV) systems. The proposed strategy is a combination of a local controller and a distributed cooperative reactive power controller with a dynamic leader, which tracks the location of the critical bus(es), to coordinate all PV systems for voltage regulation. In this strategy, the local controller calculates each PV system's initial reactive power ratio that is applied to the distributed control. Then, unlike the existing distributed control method for voltage control, our proposed algorithm dynamically finds the PV system at the critical bus using the local data and neighbors’ information transferred via communication links. The PV system at the critical bus serves as the leader agent and coordinates all PV systems through distributed cooperative control. Indeed, this approach can improve the voltage profile of the LV distribution network in both normal and voltage ride-through conditions with a fair use of each PV inverter's reactive power capacity. Moreover, this method preserves plug-and-play capability and is robust to network reconfiguration, communication malfunction, and variation of loads and weather conditions (variation of the maximum active power and the reactive power capacity of PV inverters). Simulation results are exhibited to validate the successfulness of the proposed control method under ideal and delayed communication networks. It should be noted that the proposed approach is scalable to multiple feeders with several branches.