Pedro P. Vergara

Towards a real-time Energy Management System for a Microgrid using a multi-objective genetic algorithm

This paper proposes a real-time Energy Management System (EMS) for a low voltage (LV) Microgrid (MG). The system operation consists in solving the Unit Commitment (UC) and Economic Load Dispatch (ELD) simultaneously for 24 hours ahead at every 15-minute period. This operation is formulated as a multi-objective optimization problem where the minimization of operational cost, total emissions and power losses is simultaneously pursued using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). In this algorithm, crossover and mutation operators were improved with respect to existing approaches to achieve an adequate characterization of the energy management problem and a good algorithm performance. Simulation studies have outlined that, in fact, the NSGA-II can be used as a real-time optimization tool providing a good-quality Pareto front to operate optimally the MG in a limited time of 15 minutes.

Increasing the PV hosting capacity with OLTC technology and PV VAr absorption in a MV/LV rural Brazilian distribution system

Distributed Generation (DG) can provide multiple benefits in MV/LV distribution networks. However, high penetration levels may cause technical issues to the grid, as already observed in some countries. Thus, Distribution Network Operators (DNOs) have to consider alternative solutions to guarantee normal operation, even under high penetration levels. In this paper, a time-series based probabilistic methodology is used to estimate the maximum PV hosting capacity of LV distribution networks. The proposed method is applied to assess a real rural Brazilian distribution system. According to the obtained results, voltage regulation and overloads in distribution transformers limit the PV hosting capacity. Therefore, aiming to increase this capacity, the use of PV VAr absorption and OLTC transformers was technically evaluated.

Distributed Strategy for Optimal Dispatch of Unbalanced Three-Phase Islanded Microgrids

This paper presents a distributed strategy for the optimal dispatch of islanded microgrids, modeled as unbalanced three-phase electrical distribution systems. To set the dispatch of the distributed generation (DG) units, an optimal generation problem is stated and solved distributively based on primal–dual constrained decomposition and a first-order consensus protocol, where units can communicate only with their neighbors. Thus, convergence is guaranteed under the common convexity assumptions. The islanded microgrid operates with the standard hierarchical control scheme, where two control modes are considered for the DG units: a voltage control mode, with an active droop control loop, and a power control mode, which allows setting the output power in advance. To assess the effectiveness and flexibility of the proposed approach, simulations were performed in a 25-bus unbalanced three-phase microgrid. According to the obtained results, the proposed strategy achieves a lower cost solution when compared with a centralized approach based on a static droop framework, with a considerable reduction on the communication system complexity. Additionally, it corrects the mismatch between generation and consumption even during the execution of the optimization process, responding to changes in the load consumption, renewable generation, and unexpected faults in units.

Distributed consensus-based economic dispatch considering grid operation

In this paper, an optimal distributed approach is presented to solve the economic dispatch (ED) problem in an islanded microgrid. Active and reactive resources are taken into account in the optimization problem aiming to consider the operational requirements of the electrical system. The optimization framework is based on primal-dual constrained decomposition. Additionally, two consensus algorithms are develop to be executed in parallel by each unit, aiming to estimate locally the value of the dual variables. Convergence is guaranteed under the common convexity assumptions. Simulations were performed on the standard IEEE 30-bus system considering dispatchable and renewable generation units. The capability of the algorithm to responds in variations in load consumption, renewable generation and faults in units were assessed. Result shows the flexibility and effectiveness of the proposed approach.

An MILP model for optimal management of energy consumption and comfort in smart buildings

This paper presents a new mixed integer linear programing (MILP) model for the management of energy consumption and comfort in smart buildings. Initially, a detailed mixed integer non-linear programming (MINLP) model is formulated. The approach considers the management of heating, ventilation and air conditioning (HVAC) units, lighting appliances, photovoltaic generation (PV) and energy storage system (ESS). Then, a set of linear and equivalent representations are used to approximate the problem by an MILP model. The aims of the proposed model is to minimize the electricity bill by managing the loads, as well as the schedule of the ESS, meanwhile comfortable indoor conditions are ensured by a set of mathematical constraints. A commercial MILP solver was used to guarantee optimality. The strategy was tested in an university building with multiple zones. Comparisons between the proposed MILP model and simulations in EnergyPlus were used to validate the results.

Local hierarchical control for industrial microgrids with improved frequency regulation

Local control strategies that operate without relying on communication systems enhance flexibility and reliability of AC industrial microgrids. Based on a previous work in which a secondary switched control was proposed, this paper presents a complementary strategy to improve the frequency regulation by reducing the maximum error. To this end, a dynamic-gain droop method driven by a time protocol is used. With this proposal, the maximum frequency error is effectively reduced without relying on complex techniques and maintaining the simplicity of the basis strategy and the non-use of communications. Experimental results obtained on a laboratory microgrid are presented to validate the performance of the proposed complementary control strategy.

Optimal Operation of Radial Distribution Systems Using Extended Dynamic Programming

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.

Generalization of the λ-method for decentralized economic dispatch considering reactive resources

In this paper, a generalization of the λ-Method for the economic dispatch (ED) problem in an islanded electrical distribution system (EDS) is presented. The proposed approach is based on primal-dual constrained decomposition, which allows decomposing the centralized problem into small-scale problems that can be solved in parallel and independently by each unit. Active and reactive resources are taken into account in order to consider the technical operation of the EDS. A technical implementation based on the classic droop control is also proposed, in order to reduce the communication needs of the units and the leader unit. To assess the performance of the proposed approach, simulations were performed on the IEEE 118-bus system. The capability of the algorithm to respond to unexpected changes in load consumption, non-dispatchable generation, and faults in units were assessed. Finally, the flexibility and model independence characteristics of this approach are also shown considering prohibited operational zones (POZ) for some units.

Optimal Operation of Unbalanced Three-Phase Islanded Droop-Based Microgrids

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.

Optimal schedule of dispatchable DG in electrical distribution systems with extended dynamic programming

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.