In this paper, a generalized formulation for intelligent energy management of a microgrid is proposed using artificial intelligence techniques jointly with linear-programming-based multiobjective optimization. The proposed multiobjective intelligent energy management aims to minimize the operation cost and the environmental impact of a microgrid, taking into account its preoperational variables as future availability of renewable energies and load demand (LD). An artificial neural network ensemble is developed to predict 24-h-ahead photovoltaic generation and 1-h-ahead wind power generation and LD. The proposed machine learning is characterized by enhanced learning model and generalization capability. The efficiency of the microgrid operation strongly depends on the battery scheduling process, which cannot be achieved through conventional optimization formulation. In this paper, a fuzzy logic expert system is used for battery scheduling. The proposed approach can handle uncertainties regarding to the fuzzy environment of the overall microgrid operation and the uncertainty related to the forecasted parameters. The results show considerable minimization on operation cost and emission level compared to literature microgrid energy management approaches based on opportunity charging and Heuristic Flowchart (HF) battery management.

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Multiobjective Intelligent Energy Management for a Microgrid

This paper addresses the bidding problem faced by a virtual power plant (VPP) in a joint market of energy and spinning reserve service. The proposed bidding strategy is a non-equilibrium model based on the deterministic price-based unit commitment (PBUC) which takes the supply-demand balancing constraint and security constraints of VPP itself into account. The presented model creates a single operating profile from a composite of the parameters characterizing each distributed energy resources (DER), which is a component of VPP, and incorporates network constraints into its description of the capabilities of the portfolio. The presented model is a nonlinear mixed-integer programming with inter-temporal constraints and solved by genetic algorithm (GA).

Bidding Strategy of Virtual Power Plant for Participating in Energy and Spinning Reserve Markets—Part I: Problem Formulation

A new formulation is required to provide a proper power flow analysis in islanded microgrids taking into consideration their special philosophy of operation. In this paper, a novel and generic three-phase power flow algorithm is formulated for islanded microgrids. The algorithm is novel since it adapts the real characteristics of the islanded microgrid operation; i.e., 1) some of the distributed generation (DG) units are controlled using the droop control methods and their generated active and reactive power are dependent on the power flow variables; 2) the steady-state system frequency is considered as one of the power flow variables. The proposed algorithm is generic, where the features of distribution systems, i.e., three-phase feeder models, unbalanced loads and load models have been taken in consideration. Further, all possible operation modes of DG units (droop, PV, or PQ) have been considered. The problem has been formulated as a set of nonlinear equations. A globally convergent Newton-trust region method has been proposed to solve this set of nonlinear equations. The proposed algorithm is a helpful tool to perform accurate steady state studies of the islanded microgrid. Different case studies have been carried out to test the effectiveness and the robustness of the proposed algorithm.

A Novel and Generalized Three-Phase Power Flow Algorithm for Islanded Microgrids Using a Newton Trust Region Method

In this paper, an economic dispatch model, which can take into account the uncertainties of plug-in electric vehicles (PEVs) and wind generators, is developed. A simulation based approach is first employed to study the probability distributions of the charge/discharge behaviors of PEVs. The probability distribution of wind power is also derived based on the assumption that the wind speed follows the Rayleigh distribution. The mathematical expectations of the generation costs of wind power and V2G (vehicle to grid) power are then derived analytically. An optimization algorithm is developed based on the well-established particle swarm optimization (PSO) and interior point method to solve the economic dispatch model. The proposed approach is demonstrated by the IEEE 118-bus test system.

Optimal Dispatch of Electric Vehicles and Wind Power Using Enhanced Particle Swarm Optimization

Decision making of a virtual power plant under uncertainties for bidding in a day-ahead market using point estimate method

Distributed generation modeling for power flow studies and a three-phase unbalanced power flow solution for radial distribution systems considering distributed generation

https://www.tpbin.com/Uploads/Subjects/b8ca44c7-420d-4159-b4a3-361a81290016.pdf

Integration of distributed energy resources into low voltage grid: A market-based multiperiod optimization model

This paper proposes a three-phase backward/forward power flow solution which can handle distribution transformers of various common winding connections in unbalanced system of multi voltage levels. The proposed algorithm is based on the power summation method and takes the power losses into account considering the mutual coupling between three phases. The transformers are modeled by transformer nodal admittance matrix and mathematical formulas concerning to backward/forward sweep calculations of transformers are presented in detail for power summation-based process. The validity and effectiveness of the proposed method is demonstrated by the test results.

Elahe Mashhour

Papers

Distributed generation modeling for power flow studies and a three-phase unbalanced power flow solution for radial distribution systems considering distributed generation

Integration of distributed energy resources into low voltage grid: A market-based multiperiod optimization model

Three-phase backward/forward power flow solution considering three-phase distribution transformers