Linear optimal power flow (OPF) formulations are powerful tools applied to a large number of problems in power systems, e.g., economic dispatch, expansion planning, state estimation, congestion management, electricity markets, among others. This article proposes a novel mixed-integer linear programming formulation for the ac-OPF of three-phase unbalanced distribution networks. The model aims to minimize the total energy production cost while guaranteeing the network's voltage and current magnitude operational limits. New approximations of the Euclidean norm, which is present in the calculation of nodal voltage and branch current magnitudes, are introduced by applying a linear transformation of weighted norms and a set of intersecting planes. The accuracy, optimality, feasibility, and scalability of the proposed linearizations are compared with common linear approximations in the literature using two unbalanced distribution test systems. The obtained results show that the proposed formulation is computationally more efficient (almost twice) while being as accurate and more conservative than the benchmarked approaches with maximum errors lower than 0.1%. Thus, its potential application in a variety of distribution systems operation and planning optimization problems is endorsed.

This paper presents a new linear optimal power flow model for three-phase unbalanced electrical distribution systems considering binary variables. The proposed formulation is a mixed-integer linear programming problem, aiming at minimizing the operational costs of the network while guaranteeing operational constraints. Two new linearizations for branch current and nodal voltage magnitudes are introduced. The proposed branch current magnitude linearization provides a discretization of the Euclidean norm through a set of intersecting planes, while the bus voltage magnitude approximation uses a linear combination of the L1 and the L norm. The proposed approach is compared to a nonlinear power flow for an unbalanced distribution system with fixed power injections. The obtained results showed errors of less than 4% for currents and 0.005% for voltages, demonstrating that satisfactory accuracy may be obtained using the proposed linearizations.

This paper proposes a two-stage stochastic market clearing (SMC) model based on a semidefinite programming (SDP) relaxation. The SMC model aims at determining the day-ahead schedule (DA) and the real-time (RT) balance settlement that minimize the total expected production cost. The network capacity constraints are considered in the proposed model through an AC power flow formulation, while the uncertainty in the renewable-based generation is taking into account using a set of stochastic scenarios. In order to solve the proposed non-linear programming model, a SDP relaxation is used. An illustrative example (3-bus test system) and the IEEE Reliability 24-bus test system are used to show the effectiveness and accuracy of the proposed model. Results shown that the proposed SDP relaxation introduce a negligible error, when compared with the solution after solving the original non-linear model.

This paper presents a new mixed-integer nonlinear programming (MINLP) model for the optimal operation of unbalanced three-phase droop-based microgrids. The proposed MINLP model can be seen as an extension of an optimal power flow for microgrids operating in islanded mode, that aims to minimize the total amount of unsupplied demand and the total distributed generator (DG) generation cost. Since the slack bus concept is not longer valid, the proposed model considers the frequency and voltage magnitude reference as variables. In this case, DGs units operate with droop control to balance the system and provide a frequency and voltage magnitude reference. Additionally, a set of efficient linearizations are introduced in order to approximate the original MINLP problem into a mixed-integer linear programming (MILP) model that can be solved using commercial solvers. The proposed model has been tested in a 25-bus unbalanced three-phase microgrid and a large 124-node grid, considering different operational and time-coupling constraints for the DGs and the battery systems (BSs). Load curtailment and different modes of operation for the wind turbines have also been tested. Finally, an error assessment between the original MINLP and the approximated MILP model has been conducted.

In this paper, the economic impact of the active power droop gain in droop-based microgrids has been studied. To accomplish this, a theoretical analysis is presented first. This analysis is used to study a simple case in which the solution of the economic dispatch problem is compared with the dispatch obtained after applying the standard definition of the droop gains for the droop-controlled DGs, given by the Standard IEEE 1547.7. Then, the mathematical model for the optimal power flow of droop-based islanded microgrids is used to simulate and study a real unbalanced three-phase system. Results have shown that the standard definition guarantees a proper active power sharing among all the DG units, independent of the load level. However, as shown by the simulations, the standard definition does not necessarily correspond to the solution with minimum active power losses and can have a significant economic impact when compared with the optimal economic dispatch solution.

In this paper, a new and generalized model for the optimal operation of microgrids is presented. The proposed mathematical model considers both the grid-connected (GC) and islanded (IS) operational modes. First, a mixed integer non-linear programming (MINLP) formulation is introduced, modeling the microgrid as an unbalanced ac three-phase electrical distribution system, comprising distributed generator (DG) units, battery systems and wind turbines. In GC mode, the frequency and the voltage magnitude references are imposed by the main grid at the point of common couple, while in IS mode, it is assumed that the DG units operate with droop control. Additionally, a set of convexification procedures are introduced in order to approximate the original MINLP model into a new convex formulation that can be solved using commercial solvers. The proposed model has been tested in a 25-bus microgrid for different scenarios, including one where a degradation of the voltage magnitude reference is observed. Results show that the proposed model is able to properly define the operational mode of the microgrid, based on the technical constraints of the system.

An extended dynamic programming (EDP) approach is developed to optimize the ac steady-state operation of radial electrical distribution systems (EDS). Based on the optimality principle of the recursive Hamilton-Jacobi-Bellman equations, the proposed EDP approach determines the optimal operation of the EDS by setting the values of the controllable variables at each time period. A suitable definition for the stages of the problem makes it possible to represent the optimal ac power flow of radial EDS as a dynamic programming problem, wherein the 'curse of dimensionality' is a minor concern, since the number of state and control variables at each stage is low and the time complexity of the algorithm grows linearly with the number of nodes of the EDS. The proposed EDP is applied to solve the economic dispatch of the DG units installed in a radial EDS. The effectiveness and the scalability of the EDP approach is illustrated using real-scale systems and comparisons with commercial programming solvers. Finally, generalizations to consider other EDS operation problems are also discussed.

In this chapter the most significant characteristics and functionalities of an energy management system (EMS) for microgrids are introduced. For this, the definitions of hierarchical control layers are considered. First, the main concepts and modules of the hierarchical control structure of a generalized EMS are presented. Then, energy management function is represented as an optimization problem, described as the simultaneous solution of both, a unit commitment problem and an economic load dispatch problem. An extension of the energy management problem is also formulated based on an optimal power flow. Second, the advantages and disadvantages of using either a centralized or a decentralized EMS approach are discussed. Finally, since the energy management problem is represented as an optimization problem, the most common methodologies and solution algorithms used in the specialized literature are discussed, including metaheuristics, mixedinteger linear approximations, and nonlinear approaches, as well as software tools for implementing models and simulations.

This paper presents a mixed-integer linear programming (MILP) model for the optimal energy management of residential microgrids, modeled as unbalanced, three-phase, electrical distribution system (EDS). Initially, the problem is formulated as a mixed-integer nonlinear programming (MINLP) problem. Then, a set of linear approximations and equivalent mathematical representations are used to obtain a precise MILP model. The proposed formulation considers three-phase generation units (GU), single-phase photovoltaic (PV) resources, and single-phase energy storage systems (ESS), as well as load management. The aim of the proposed model is to minimize the final operational costs of the microgrid while considering operational constraints of the EDS and an unexpected outage of the main grid through a security-constrained set of equations. The optimal solution of the MILP model is found using commercial convex optimization solvers. The proposed model was tested in a residential, three-phase EDS. Results show that the proposed linearizations and approximations produce accurate solutions when compared with a nonlinear three-phase OPF formulation, with an error in the objective function near to 2% and a maximum error in the voltage near to 1%. Efficiency and flexibility of the proposed methodology are also discussed.

In this paper, the optimal schedule of dispatchable distributed generation (DG) units connected to radial electrical distribution systems (EDS) is solved using an extended dynamic programming approach. The objective of the optimal DG scheduling problem is to determine the hour-by-hour active generation output of each dispatchable DG unit, in order to minimize the total active power losses of the EDS and the generation costs. The proposed extended dynamic programming (EDP) is an advantageous approach because convexity is not required to obtain a global optimal solution, and the 'curse of dimensionality' is not a concern since the computational complexity of the algorithm grows linearly with the size of the network. Besides, the state variables have only two dimensions, one to represent the active power flows and the other to represent the nodal voltages. A 56-nodes MV distribution system with two dispatchable DG units is used to evaluate the performance of the proposed EDP approach, considering a deterministic and a stochastic case. A set of Monte Carlo simulations is used to analyze the influence of uncertainties. Results confirm that the proposed methodology is a suitable approach to unveil the best operation schedule for dispatchable DG units.