This paper presents an energy management system (EMS) for single-phase or balanced three-phase microgrids via robust convex optimization. Along a finite planning horizon, the solution provided by the proposed microgrids EMS remains feasible under adverse conditions of random demands and renewable energy resources. The proposed model is represented as a convex mixed-integer second-order cone programming model. Two operation modes are considered: grid-connected and isolated modes. In grid-connected mode, the proposed EMS minimizes the costs of energy imports, dispatches of distributed generation (DG) units, and the operation of the energy storage systems. In isolated mode, the proposed EMS minimizes the unsupplied demand, considering consumer priorities. Global robustness of the proposed mathematical model is adjusted using a single parameter ζ. The robustness of the solutions provided by the robust EMS is assessed using the Monte Carlo simulation method. In this case, DG units are set to operate in frequency and voltage droop control to support network fluctuations. Simulations are deployed using a microgrid with 136-nodes and several distributed energy resources. Results showed that the proposed model is suitable for the short-term microgrids energy management system. The robustness of the final solution was directly proportional to the operational costs, and it can be effectively controlled by the proposed parameter ζ for both operation modes. When compared to stochastic approaches, the proposed formulation showed to be more flexible and less time-consuming.

In this paper, a two-stage procedure is proposed in order to solve the centralized self-healing scheme for electrical distribution systems. The considered self-healing actions are the reconfiguration of the distribution grid and, if needed, node and zone load-shedding. Thus, the proposed procedure determines the status of the switching devices in order to effectively isolate a faulty zone and minimize the number of de-energized nodes and zones, while ensuring that the operative and electrical constraints of the system are not violated. The proposed method is comprised of two stages. The first stage solves a mixed integer linear programming (MILP) problem in order to obtain the binary decision variables for the self-healing scheme (i.e., the switching device status and energized zones). In the second stage, a nonlinear programming (NLP) problem is solved in order to adjust the steady-state operating point of the topology found in the first stage (i.e., correction of the continuous electrical variables and load-shedding optimization). Commercial optimization solvers are used in the first stage to solve the MILP problem and in the second stage to solve the NLP problem. A 44-node test system and a real Brazilian distribution system with 964-nodes were used to test and verify the proposed methodology.

The optimal operation planning (OOP) of electrical distribution systems (EDS) is very sensible to the quality of the short-term load forecasts. Assuming aggregated demands in EDS as univariate non-stationary seasonal time series, and based on historical measurements gathered by smart meters, this paper presents a parsimonious short-term load forecasting method to estimate the expected outcomes of future demands, and the standard deviations of forecast errors. The chosen short-term load forecasting method is an adaptation of the multiplicative autoregressive integrated moving average (ARIMA) models. Seasonal ARIMA models are parsimonious forecasting techniques because they require very few parameters and low computational resources to provide an adequate representation of stochastic time series. Two approaches are used in this paper to estimate the parameters that constitute the proposed multiplicative ARIMA model: a frequentist and a Bayesian approach. Advantages and disadvantages of both methods are compared by simulating a centralized self-healing scheme of a real EDS that uses the forecasts to deploy a robust restoration plan. Results shown that the proposed seasonal ARIMA model is a fast, precise, straightforward and adaptable load forecasting method, suitable for OOP of highly supervised EDS.

This work presents a non-intrusive load monitoring (NILM) algorithm based on mixed-integer linear programming (MILP). The formulation deals with the problem of multiple switching that arises when disaggregating individual appliance’s consumptions from a compound power measurement. Mixed-integer linear constraints are used to efficiently represent the load signatures of each appliance. Also, a window-based strategy is used to enhance the computational performance of the proposed NILM algorithm. The disaggregation can be made using only active power measurements at a low sampling rate, which is available in most energy meters. Moreover, if available, other signatures can be added to the model to improve its accuracy, such as reactive power signatures or harmonics. The performance of the algorithm is evaluated using three test cases from the almanac of minutely power dataset (AMPds). The proposed method is also compared with a disaggregation method called aided linear integer programming (ALIP). Results demonstrate the ability of the proposed method to accurately identify and allocate individual energy signatures in a computationally efficient manner, which makes it suitable for inexpensive home energy management.

The deployment of battery energy storage systems (BESS) is rapidly increasing as a prominent option to support future renewable-based energy systems. However, despite its benefits from a technical perspective, there are still challenges related to its economic viability. On the other hand, sizing BESS considering only their economic viability can be impractical because financial objectives could be in conflict with other aspects, such as battery degradation and grid impact. This paper proposes a multi-objective approach to determine the optimal size of BESS providing stackable services, such as frequency regulation and peak shaving. The proposed optimization method comprises financial and technical aspects represented by the payback period, battery life span, and grid impact. Given a set of market rules, a cost-benefit function, a regulation signal, consumption profiles and grid data, an enumerative approach is adopted to provide a set of Pareto optimal solutions. The performance of the proposed method is validated using the regulation market structure from PJM interconnection. Furthermore, a real 240-node distribution grid is used to assess the grid impact via OpenDSS. Simulations demonstrate that the proposed approach is a flexible and practical decision-making tool that investors can exploit when designing new BESS.

This paper presents a mixed-integer non-linear programming (MINLP) model for the optimal restoration/maintenance switching sequence of unbalanced three-phase electrical distribution systems. Once the protection coordination has identified and cleared a faulty zone, the proposed MINLP model determines the status of remotely controlled switches and the dispatchable distributed generation (DG) units, used to de-energized the troubled section of the network and supply as much load as possible. The restoration considers the switching sequence over a discrete horizon, guaranteeing that the operational constraints of the distribution system are not violated in every step of the sequence. Furthermore, a set of linearization strategies are presented to transform the proposed MINLP model into a mixed-integer linear programming (MILP) model. The use of MILP models guarantees convergence to optimality by applying convex optimization techniques. Tests are performed on an unbalanced three-phase radial distribution system consisting of 123 nodes, 12 switches, and three dispatchable DG units. The obtained results show that the proposed optimization model is a holistic procedure that can be used to efficiently manage power restoration or to minimize isolated areas in case of scheduled maintenance in modern electrical distribution systems.