System of Systems Based Decision-Making for Power Systems Operation

A modern power system is composed of many individual entities collaborating with each other to operate the entire system in a secure and economic manner. These entities may have different owners and operators with their own operating rules and policies, and it complicates the decision-making process in the system. In this work, a system of systems (SoS) engineering framework is presented for optimally operating the modern power systems. The proposed SoS framework defines each entity as an independent system with its own regulations, and the communication and process of information exchange between the systems are discussed. Since the independent systems are working in an interconnected system, the operating condition of one may impact the operating condition of others. According to the independent systems’ characteristics and connection between them, an optimization problem is formulated for each independent system. In order to solve the optimization problem of each system and to optimally operate the entire SoS-based power system, a decentralized decision-making algorithm is developed. Using this algorithm, only a limited amount of information is exchanged among different systems, and the operators of independent systems do not need to exchange all the information, which may be commercially sensitive, with each other. In addition, applying chance-constrained stochastic programming, the impact of uncertain variables, such as renewable generation and load demands, is modeled in the proposed SoS-based decision-making algorithm. The proposed SoS-based decision-making algorithm is applied to find the optimal and secure operating point of an active distribution grid (ADG). This SoS framework models the distribution company (DISCO) and microgrids (MGs) as independent systems having the right to work based on their own operating rules and policies, and it coordinates the DISCO and MGs operating condition. The proposed decision-making algorithm is also performed to solve the security-constrained unit commitment incorporating distributed generations (DGs) located in ADGs. The independent system operator (ISO) and DISCO are modeled as self-governing systems, and competition and collaboration between them are explained according to the SoS framework

Trainable Variational Quantum-Multiblock ADMM Algorithm for Generation Scheduling

The advent of quantum computing can potentially revolutionize how complex problems are solved. This paper proposes a two-loop quantum-classical solution algorithm for generation scheduling by infusing quantum computing, machine learning, and distributed optimization. The aim is to facilitate employing noisy near-term quantum machines with a limited number of qubits to solve practical power system optimization problems such as generation scheduling. The outer loop is a 3-block quantum alternative direction method of multipliers (QADMM) algorithm that decomposes the generation scheduling problem into three subproblems, including one quadratically unconstrained binary optimization (QUBO) and two non-QUBOs. The inner loop is a trainable quantum approximate optimization algorithm (T-QAOA) for solving QUBO on a quantum computer. The proposed T-QAOA translates interactions of quantum-classical machines as sequential information and uses a recurrent neural network to estimate variational parameters of the quantum circuit with a proper sampling technique. T-QAOA determines the QUBO solution in a few quantum-learner iterations instead of hundreds of iterations needed for a quantum-classical solver. The outer 3-block ADMM coordinates QUBO and non-QUBO solutions to obtain the solution to the original problem. The conditions under which the proposed QADMM is guaranteed to converge are discussed. Two mathematical and three generation scheduling cases are studied. Analyses performed on quantum simulators and classical computers show the effectiveness of the proposed algorithm. The advantages of T-QAOA are discussed and numerically compared with QAOA which uses a stochastic gradient descent-based optimizer.Comment: 11 page

Accelerating L-shaped Two-stage Stochastic SCUC with Learning Integrated Benders Decomposition

Benders decomposition is widely used to solve large mixed-integer problems. This paper takes advantage of machine learning and proposes enhanced variants of Benders decomposition for solving two-stage stochastic security-constrained unit commitment (SCUC). The problem is decomposed into a master problem and subproblems corresponding to a load scenario. The goal is to reduce the computational costs and memory usage of Benders decomposition by creating tighter cuts and reducing the size of the master problem. Three approaches are proposed, namely regression Benders, classification Benders, and regression-classification Benders. A regressor reads load profile scenarios and predicts subproblem objective function proxy variables to form tighter cuts for the master problem. A criterion is defined to measure the level of usefulness of cuts with respect to their contribution to lower bound improvement. Useful cuts that contain the necessary information to form the feasible region are identified with and without a classification learner. Useful cuts are iteratively added to the master problem, and non-useful cuts are discarded to reduce the computational burden of each Benders iteration. Simulation studies on multiple test systems show the effectiveness of the proposed learning-aided Benders decomposition for solving two-stage SCUC as compared to conventional multi-cut Benders decomposition

Hybrid Learning Aided Inactive Constraints Filtering Algorithm to Enhance AC OPF Solution Time

The Optimal power flow (OPF) problem contains many constraints. However, equality constraints along with a limited set of active inequality constraints encompass sufficient information to determine the feasible space of the problem. In this paper, a hybrid supervised regression and classification learning based algorithm is proposed to identify active and inactive sets of inequality constraints of AC OPF solely based on nodal power demand information. The proposed algorithm is structured using several classifiers and regression learners. The combination of classifiers with regression learners enhances the accuracy of active / inactive constraints identification procedure. The proposed algorithm modifies the OPF feasible space rather than a direct mapping of OPF results from demand. Inactive constraints are removed from the design space to construct a truncated AC OPF. This truncated optimization problem can be solved faster than the original problem with less computational resources. Numerical results on several test systems show the effectiveness of the proposed algorithm for predicting active and inactive constraints and constructing a truncated AC OPF. We have posted our code for all simulations on arxiv and have uploaded the data used in numerical studies to IEEE DataPort as an open access dataset.Comment: Pages:

Temporal Decomposition-Based Stochastic Economic Dispatch for Smart Grid Energy Management

This paper presents a temporal decomposition strategy to decompose security-constrained economic dispatch (SCED) over the scheduling horizon with the goal of reducing its computational burden and enhancing its scalability. A set of subproblems, each with respect to demand response, normal constraints, and N-1 contingency corrective actions at a subhorizon, is formulated. The proposed decomposition deals with computational complexities originated from intertemporal interdependencies of system equipment, i.e., generators\u27 ramp constraints and state of charge of storage devices. The concept of overlapping intervals is introduced to make SCED subproblems solvable in parallel. Intertemporal connectivity related to energy storage is also modeled in the context of temporal decomposition. Besides, reserve up and down requirements are formulated as data-driven nonparametric chance constraints to account for wind generation uncertainties. The concept of \phi - divergence is used to convert nonparametric chance constraints to more conservative parametric constraints. A reduced risk level is calculated with respect to wind generation prediction errors to ensure the satisfaction of system constraints with a confidence level after the true realization of uncertainty. Auxiliary problem principle is applied to coordinate SCED subproblems in parallel. Numerical results on three test systems show the effectiveness of the proposed algorithm

Time decomposition strategy for securityconstrained economic dispatch

A horizontal time decomposition strategy to reduce the computation time of security-constrained economic dispatch (SCED) is presented in this study. The proposed decomposition strategy is fundamentally novel and is developed in this paper for the first time. The considered scheduling horizon is decomposed into multiple smaller sub-horizons. The concept of overlapping time intervals is introduced to model ramp constraints for the transition from one sub-horizon to another subhorizon. A sub-horizon includes several internal intervals and one or two overlapping time intervals that interconnect consecutive sub-horizons. A local SCED is formulated for each sub-horizon with respect to internal and overlapping intervals\u27 variables/constraints. The overlapping intervals allow modelling intertemporal constraints between the consecutive sub-horizons in a distributed fashion. To coordinate the subproblems and find the optimal solution for the whole operation horizon distributedly, accelerated auxiliary problem principle is developed. Furthermore, the authors present an initialisation strategy to enhance the convergence performance of the coordination strategy. The proposed algorithm is applied to three large systems, and promising results are obtained

Optimal sizing of energy storage systems: A combination of hourly and intra-hour time perspectives

Storage technology is a key enabler for the integration of renewable energy resources into power systems because it provides the required flexibility to balance, the net load variability and forms a buffer for uncertainties. A solution for sizing of energy storage devices in electric power systems is presented. The considered planning problem is divided into two time perspectives: hourly and intra-hour intervals. For the intra-hour time horizon, the algorithm determines the optimal size of the energy storage devices to provide the adequate ramping capability for the system. This ramping capability guarantees the system ability to follow the load in the intra-hour intervals, as well as to alleviate short-term wind generation and load fluctuations. In the hourly time scale, the optimal size of the storage is determined with respect to having a sufficient generation capacity to support the loads. A 6-bus test power system is studied to show the effectiveness of the proposed algorithm

Learning-aided Asynchronous ADMM for Optimal Power Flow

The synchronization requirement is a bottleneck of many distributed optimization algorithms, particularly for solving problems with computationally heterogeneous subproblems and during the occurrence of communication failure/delay. This paper presents a double-loop learning-aided asynchronous alternating direction method of multipliers (LA-ADMM) that has information prediction capability and handles a considerable level of asynchrony between subproblems. A momentum-extrapolation prediction-correction technique is developed to enable subproblems to predict their neighbors missing shared variable information instead of using the latest received values. An online streaming-based anomaly classification is designed to observe the performance of predicted data and control Lagrange multipliers update over the course of iterations. The proposed LA-ADMM reduces under-utilization of computation resources, especially if subproblems are computationally heterogeneous. This algorithm also enhances distributed optimization robustness against communication failure/delay that may result in a considerable level of asynchrony between subproblems. LA-ADMM is applied to solve the optimal power flow problem for several test systems. Promising results are obtained as compared to the classical synchronous ADMM and asynchronous ADMM without the anomaly switch control

Topology-aware Learning Assisted Branch and Ramp Constraints Screening for Dynamic Economic Dispatch

Multi-interval or dynamic economic dispatch (D-ED) is the core of various power system management functions. This optimization problem contains many constraints, a small subset of which is sufficient to enclose the D-ED feasible region. This paper presents a topology-aware learning-aided iterative constraint screening algorithm to identify a feasibility outlining subset of network and generating units ramp up/down constraints and create a truncated D-ED problem. We create a colorful image from nodal demand, thermal unit generation cost, and network topology information. Convolutional neural networks are trained for constraint status identification using colorful images corresponding to system operating conditions and transfer learning. Filtering inactive line flow and ramp up/down constraints reduces optimizations size and computational burden, resulting in a reduction in solution time and memory usage. Dropping all inactive branch and ramp constraints may activate some of these originally inactive constraints upon solving the truncated D-ED. A loop is added to form a constraints coefficient matrix iteratively during training dataset preparation and algorithm utilization. This iterative loop guarantees truncated D-ED results feasibility and optimality. Numerical results show the proposed algorithms effectiveness in constraint status prediction and reducing the size and solution time of D-ED