A Novel Consensus-based Distributed Algorithm for Economic Dispatch Based on Local Estimation of Power Mismatch

This paper proposes a novel consensus-based distributed control algorithm for solving the economic dispatch problem of distributed generators. A legacy central controller can be eliminated in order to avoid a single point of failure, relieve computational burden, maintain data privacy, and support plug-and-play functionalities. The optimal economic dispatch is achieved by allowing the iterative coordination of local agents (consumers and distributed generators). As coordination information, the local estimation of power mismatch is shared among distributed generators through communication networks and does not contain any private information, ultimately contributing to a fair electricity market. Additionally, the proposed distributed algorithm is particularly designed for easy implementation and configuration of a large number of agents in which the distributed decision making can be implemented in a simple proportional-integral (PI) or integral (I) controller. In MATLAB/Simulink simulation, the accuracy of the proposed distributed algorithm is demonstrated in a 29-node system in comparison with the centralized algorithm. Scalability and a fast convergence rate are also demonstrated in a 1400-node case study. Further, the experimental test demonstrates the practical performance of the proposed distributed algorithm using the VOLTTRON platform and a cluster of low-cost credit-card-size single-board PCs.Comment: 16 Pages, 13 figures Figures order and references are corrected

Distributed Dynamic Economic Dispatch using Alternating Direction Method of Multipliers

With the proliferation of distributed energy resources and the volume of data stored due to advancement in metering infrastructure, energy management in power system operation needs distributed computing. In this paper, we propose a fully distributed Alternating Direction Method of Multipliers (ADMM) algorithm to solve the distributed economic dispatch (ED) problem, where the optimization problem is fully decomposed between participating agents. In our proposed framework, each agent estimates the dual variable and the average of the total power mismatch of the network using dynamic average consensus, which replaces the dual updater in the traditional ADMM with a distributed alternative. Unlike other distributed ADMM, the proposed method does not rely on any specific assumption and captures the real-time demand change. The algorithm is validated successfully via case studies for IEEE 30-bus and 300-bus test systems with the penetration of solar photovoltaic.Comment: Accepted for 2020 Applied Energy Symposium (MITAB

Distributed Resource Allocation Over Dynamic Networks with Uncertainty

Motivated by broad applications in various fields of engineering, we study a network resource allocation problem where the goal is to optimally allocate a fixed quantity of resources over a network of nodes. We consider large scale networks with complex interconnection structures, thus any solution must be implemented in parallel and based only on local data resulting in a need for distributed algorithms. In this paper, we study a distributed Lagrangian method for such problems. By utilizing the so-called distributed subgradient methods to solve the dual problem, our approach eliminates the need for central coordination in updating the dual variables, which is often required in classic Lagrangian methods. Our focus is to understand the performance of this distributed algorithm when the number of resources is unknown and may be time-varying. In particular, we obtain an upper bound on the convergence rate of the algorithm to the optimal value, in expectation, as a function of the size and the topology of the underlying network. The effectiveness of the proposed method is demonstrated by its application to the economic dispatch problem in power systems, with simulations completed on the benchmark IEEE-14 and IEEE-118 bus test systems

Distributed Lagrangian Methods for Network Resource Allocation

Motivated by a variety of applications in control engineering and information sciences, we study network resource allocation problems where the goal is to optimally allocate a fixed amount of resource over a network of nodes. In these problems, due to the large scale of the network and complicated inter-connections between nodes, any solution must be implemented in parallel and based only on local data resulting in a need for distributed algorithms. In this paper, we propose a novel distributed Lagrangian method, which requires only local computation and communication. Our focus is to understand the performance of this algorithm on the underlying network topology. Specifically, we obtain an upper bound on the rate of convergence of the algorithm as a function of the size and the topology of the underlying network. The effectiveness and applicability of the proposed method is demonstrated by its use in solving the important economic dispatch problem in power systems, specifically on the benchmark IEEE-14 and IEEE-118 bus systems

Initialization-free Distributed Algorithms for Optimal Resource Allocation with Feasibility Constraints and its Application to Economic Dispatch of Power Systems

In this paper, the distributed resource allocation optimization problem is investigated. The allocation decisions are made to minimize the sum of all the agents' local objective functions while satisfying both the global network resource constraint and the local allocation feasibility constraints. Here the data corresponding to each agent in this separable optimization problem, such as the network resources, the local allocation feasibility constraint, and the local objective function, is only accessible to individual agent and cannot be shared with others, which renders new challenges in this distributed optimization problem. Based on either projection or differentiated projection, two classes of continuous-time algorithms are proposed to solve this distributed optimization problem in an initialization-free and scalable manner. Thus, no re-initialization is required even if the operation environment or network configuration is changed, making it possible to achieve a "plug-and-play" optimal operation of networked heterogeneous agents. The algorithm convergence is guaranteed for strictly convex objective functions, and the exponential convergence is proved for strongly convex functions without local constraints. Then the proposed algorithm is applied to the distributed economic dispatch problem in power grids, to demonstrate how it can achieve the global optimum in a scalable way, even when the generation cost, or system load, or network configuration, is changing.Comment: 13 pages, 7 figure

Storage Sizing and Placement through Operational and Uncertainty-Aware Simulations

As the penetration level of transmission-scale time-intermittent renewable generation resources increases, control of flexible resources will become important to mitigating the fluctuations due to these new renewable resources. Flexible resources may include new or existing synchronous generators as well as new energy storage devices. Optimal placement and sizing of energy storage to minimize costs of integrating renewable resources is a difficult optimization problem. Further,optimal planning procedures typically do not consider the effect of the time dependence of operations and may lead to unsatisfactory results. Here, we use an optimal energy storage control algorithm to develop a heuristic procedure for energy storage placement and sizing. We perform operational simulation under various time profiles of intermittent generation, loads and interchanges (artificially generated or from historical data) and accumulate statistics of the usage of storage at each node under the optimal dispatch. We develop a greedy heuristic based on the accumulated statistics to obtain a minimal set of nodes for storage placement. The quality of the heuristic is explored by comparing our results to the obvious heuristic of placing storage at the renewables for IEEE benchmarks and real-world network topologies.Comment: To Appear in proceedings of Hawaii International Conference on System Sciences (HICSS-2014

Review of trends and targets of complex systems for power system optimization

Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107

A Logic-Based Mixed-Integer Nonlinear Programming Model to Solve Non-Convex and Non-Smooth Economic Dispatch Problems: An Accuracy Analysis

This paper presents a solver-friendly logic-based mixed-integer nonlinear programming model (LB-MINLP) to solve economic dispatch (ED) problems considering disjoint operating zones and valve-point effects. A simultaneous consideration of transmission losses and logical constraints in ED problems causes difficulties either in the linearization procedure, or in handling via heuristic-based approaches, and this may result in outcome violation. The non-smooth terms can make the situation even worse. On the other hand, non-convex nonlinear models with logical constraints are not solvable using the existing nonlinear commercial solvers. In order to explain and remedy these shortcomings, we proposed a novel recasting strategy to overcome the hurdle of solving such complicated problems with the aid of the existing nonlinear solvers. The proposed model can facilitate the pre-solving and probing techniques of the commercial solvers by recasting the logical constraints into the mixed-integer terms of the objective function. It consequently results in a higher accuracy of the model and better computational efficiency. The acquired results demonstrated that the LB-MINLP model, compared to the existing (heuristic-based and solver-based) models in the literature, can easily handle the non-smooth and nonlinear terms and achieve an optimal solution much faster and without any outcome violation

Distributed Constrained Optimization over Networked Systems via A Singular Perturbation Method

This paper studies a constrained optimization problem over networked systems with an undirected and connected communication topology. The algorithm proposed in this work utilizes singular perturbation, dynamic average consensus, and saddle point dynamics methods to tackle the problem for a general class of objective function and affine constraints in a fully distributed manner. It is shown that the private information of agents in the interconnected network is guaranteed in our proposed strategy. The theoretical guarantees on the optimality of the solution are provided by rigorous analyses. We apply the new proposed solution into energy networks by a demonstration of two simulations.Comment: 8 page

Stochastic-based Optimal Daily Energy Management of Microgrids in Distribution Systems

Microgrid (MG) with different technologies in distributed generations (DG) and different control facilities require proper management and scheduling strategies. In these strategies, in order to reach the optimal management, the stochastic nature of some decision variables should be considered. Therefore, we proposes a new stochastic-based method for optimal daily energy management (SDEM) of MGs considering economic and reliability aspects. The optimization aim is to minimize overall operating cost, power losses cost, pollutants emission cost and cost of energy not supply (ENS). The network is assumed to be supplied by renewable and dispatch able generators and energy storage systems (ESS). The system uncertainties are considered using a set of scenarios and a scenario reduction method is applied to enhance a trade-off between the accuracy of the solution and the computational burden. Cuckoo optimization algorithm (COA) is applied to minimize the objective function as an optimization algorithm. The effectiveness and efficiency of the proposed method are validated through extensive numerical tests on PG&E 69-bus test distribution system. The results show that the proposed framework can be considered as an efficient tool in optimal daily energy management of smart distribution networks.Comment: 7 pages, 9 figures, 2 tables, IEEE, International Conference on Control, Decision and Information Technologie