Network-constrained Unit Commitment Research Articles

Network-Constrained Unit Commitment With Flexible Temporal Resolution

Network-Constrained Unit Commitment With Flexible Temporal Resolution

AC Network-Constrained Unit Commitment Based on Adaptive Linear Power Flow Model

AC Network-Constrained Unit Commitment Based on Adaptive Linear Power Flow Model

Financial and Environmental Investigation of the Effects of the Demand Response Programs on the Network-Constrained Unit Commitment

Financial and Environmental Investigation of the Effects of the Demand Response Programs on the Network-Constrained Unit Commitment

Operation of interconnected power and freshwater networks

As freshwater demand continues to rise and conventional freshwater resources decline, the interaction between the freshwater and power sectors is becoming increasingly relevant. Coordinating both systems entail minimizing the operation costs of the power and freshwater system while supplying the demands of electricity and freshwater and complying with operational constraints. This is particularly relevant in Middle Eastern regions, which have limited availability of freshwater resources. Careful coordination of electricity generation and freshwater production is beneficial for both the power and the freshwater systems. For example, producing excess freshwater at hours of low electricity production cost and storing it for usage at hours of high electricity production cost is economically advantageous. We use detailed models for the operation of the power and freshwater systems. That is, the power system operation is represented through a network-constrained unit commitment model, while the freshwater system model includes nonlinear representations of the freshwater production and transportation. The resulting problem is computationally intractable for large-scale systems. To achieve tractability, nonlinearities in the freshwater model are linearized. The final model is a tractable mixed-integer linear optimization problem that is solved using a state-of-the-art branch-and-cut solver. Simulation results demonstrate that a coordinated scheduling results in lower cost and lower peak as compared with uncoordinated scheduling.

Day-ahead Network-constrained Unit Commitment Considering Distributional Robustness and Intraday Discreteness: A Sparse Solution Approach

Quick-start generation units are critical devices and flexible resources to ensure a high penetration level of renewable energy in power systems. By considering the wind uncertainty and both binary and continuous decisions of quick-start generation units within the intraday dispatch, we develop a Wasserstein-metric-based distributionally robust optimization model for the day-ahead network-constrained unit commitment (NCUC) problem with mixed-integer recourse. We propose two feasible frameworks for solving the optimization problem. One approximates the continuous support of random wind power with a finite number of events, and the other leverages the extremal distributions instead. Both solution frameworks rely on the classic nested column-and-constraint generation (C&CG) method. It is shown that due to the sparsity of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{1}$</tex> -norm Wasserstein metric, the continuous support of wind power generation could be represented by a discrete one with a small number of events, and the rendered extremal distributions are sparse as well. With this reduction, the distributionally robust NCUC model with complicated mixed-integer recourse problems can be efficiently handled by both solution frameworks. Numerical studies are carried out, demonstrating that the model considering quick-start generation units ensures unit commitment (UC) schedules to be more robust and cost-effective, and the distributionally robust optimization method captures the wind uncertainty well in terms of out-of-sample tests.

Boosting Transmission System Flexibility in Network-Constrained Unit Commitment by Incorporating Distributed Series Reactors

In the recent decade, renewable energy sources (RES) such as wind and photovoltaic (PV) power generations gained more attention. However, despite their proven role in the reduction of operating costs of the network, their integration has some serious challenges such as the inherent uncertainties and the energy balance at the power grid. Some options to face these challenges, as well as to enhance the network’s flexibility, are demand response (DR), distributed FACTS (D-FACTS) devices, and energy storage systems (ESS). This paper focuses on the solution of the unit commitment (UC) problem in such a way that in addition to increasing the transmission network’s flexibility, the operation cost of the network decreases. To do this, distributed series reactors (DSRs) are employed as promising D-FACTS devices to enhance the transmission network’s flexibility. The uncertainty of RES is handled by the scenario-based stochastic programming technique. A mixed-integer linear programming (MILP) approach is developed, with a guaranteed global optimal solution, by developing a convex model for power flow in the presence of DSRs. The proposed UC model is implemented on the IEEE RTS 24-bus system, and the obtained results in different cases show the ability of the abovementioned flexibilities to reduce the operation cost of the network.

A Partnership of Virtual Power Plant in Day-Ahead Energy and Reserve Markets Based on Linearized AC Network-Constrained Unit Commitment Model

This paper presents coordinated energy management as a virtual power plant (VPP) framework with a wind farm, a storage system, and a demand response program in the transmission network according to the cooperation of VPPs in day-ahead energy and reserve markets. This strategy is based on a bilevel method, where it maximizes the expected VPP revenue in the proposed markets subject to constraints of renewable and flexible sources and the VPP reserve model in the upper-level problem. Also, a market-clearing model based on network-constrained unit commitment (NCUC) is explained in the lower-level problem so that it minimizes the expected operating cost of generation units constrained to a linearized AC-NCUC model. The scenario-based stochastic programming (SBSP) models the uncertainties of loads and WF power generation. Then, the master/slave decomposition method solves the bilevel problem to achieve an optimal solution at a low computational time. Also, since the lower-level problem is mixed-integer linear programming, the Benders decomposition algorithm is adopted to solve this problem. Finally, the suggested approach is implemented on IEEE test networks in GAMS software, and numerical results confirm the efficiency of the coordinated VPP management in DA energy and reserve markets and its capabilities in improving network operation.

Maximizing wind energy utilization in smart power systems using a flexible network-constrained unit commitment through dynamic lines and transformers rating

Wind power generations as renewable and sustainable energies are widely integrated into electric power systems due to their technical, environmental, and economic advantages. This integration affects the power system operation and planning studies. Network-constrained unit commitment (NCUC) is one of important operation studies of electric power systems in which the aim is to schedule the generating units for the next 24-h period with the minimum operating cost considering the constraints of generating units and transmission network. One of the essential constraints influencing the results of NCUC is thermal rating of lines and transformers. In most of the previous studies, static ratings have been used for this equipment. The static ratings are conservative values leading to non-optimal use of real capacity of lines and transformers and increasing the system's operational costs. This issue is highlighted when there exist wind energy generations in system. A new NCUC problem is addressed in this paper that considers dynamic line rating (DLR) and dynamic transformer rating (DTR) in the presence of wind power generations. The proposed approach has been implemented on the 24-bus IEEE-RTS system in the GAMS environment in different experiments. The simulation results demonstrate effectiveness of the conducted model in reducing the operational costs and maximizing the usage of wind energy.

Coordinated operation of electricity and gas-hydrogen systems with transient gas flow and hydrogen concentration tracking

This paper addresses the problem of coordinating the operation of electricity and natural gas (NG) transmission systems with green hydrogen (H2) production and injection into existing NG networks. In particular, the operation of the two systems consists of a two-stage optimization framework that solves a network-constrained unit commitment (UC) problem with transmission power losses to obtain the profiles of gas energy demands from gas-powered generators and the maximum allowable H2 injection flow rates from power-to-gas (PtG), which are then used as inputs to an optimal transient NG-H2 flow problem with H2 concentration tracking. The nonlinearities introduced by the discretization of the H2 concentration tracking equations are particularly challenging to solve using second-order nonlinear programming (NLP) methods. Moreover, the nonlinear constraints capturing transmission line losses make the electricity operational problem intractable if solved with mixed-integer NLP methods. Therefore, this work leverages the reliability and scalability of linear programming (LP) by designing two novel and distinct sequential LP (SLP) methods that exploit the particular structures of the two problems to find feasible, possibly optimal solutions, using only first-order information. The algorithmic framework is demonstrated on the IEEE 24-bus RTS connected to the 22-node Belgian gas network. This paper is the first to demonstrate H2 concentration tracking under transient gas flow in a multi-energy optimization framework on a realistic gas transmission network with multiple H2 injection locations.

Feature-Driven Economic Improvement for Network-Constrained Unit Commitment: A Closed-Loop Predict-and-Optimize Framework

As an important application in the power system operation and electricity market clearing, the network-constrained unit commitment (NCUC) problem is usually executed by Independent System Operators (ISO) in an open-looped predict-then-optimize (O-PO) process, in which an upstream prediction (e.g., on renewable energy sources (RES) and loads) and a downstream NCUC are executed in a queue. However, in the O-PO framework, a statistically more accurate prediction may not necessarily lead to a higher NCUC economics against actual RES and load realizations. To this end, this paper presents a closed-loop predict-and-optimize (C-PO) framework for improving the NCUC economics. Specifically, the C-PO leverages structures (i.e., constraints and objective) of the NCUC model and relevant feature data to train a cost-oriented RES prediction model, in which the prediction quality is evaluated via the induced NCUC cost instead of the statistical forecast errors. Therefore, the loop between the prediction and the optimization is closed to deliver a cost-oriented RES power prediction for NCUC optimization. Lagrangian relaxation is adopted to accelerate the training process, making the C-PO applicable for real-world systems. Case studies on an IEEE RTS 24-bus system and an ISO-scale 5655-bus system with real-world data show that the proposed C-PO can effectively improve the NCUC economics as compared to the traditional O-PO.

AC Network-Constrained Unit Commitment via Relaxation and Decomposition

We propose an approach to solving the AC network-constrained unit commitment problem (AC-NCUC) of large-scale power systems. This approach relies on solving convex (continuous and mixed-integer) optimization problems. First, we formulate a second-order conic relaxation of the original optimization problem, which renders a mixed-integer second-order conic optimization problem. To solve this problem, we use Benders’ decomposition, which decomposes it into a master problem, which is mixed-integer linear, and a subproblem, which is continuous and second-order conic. To improve the convergence of the Benders’ algorithm, we add several linear feasibility constraints to the master problem. Since the second-order relaxation of the original problem cannot guarantee AC feasibility of the solution found, we ensure such feasibility by solving a sequence of continuous convex optimization problems. We numerically validate the proposed approach using a Texas 2000-bus system.

Assessment of Canada's electricity system potential for variable renewable energy integration

Meeting commitments to the Paris Agreement will necessitate a transition in Canada's power system including increased generation from renewable sources. This study analyzes the Canadian electricity system in terms of its readiness to operationalize decarbonization strategies. More specially, this paper evaluates potential of Canada's electricity system in terms of the flexibility required to integrate variable renewable energies (VREs). To do so, the SILVER production cost model (PCM) is refined to improve its representation in terms of hydro resources, demand response programs and price-setting mechanism. Next, a network-constrained unit commitment (NCUC) of SILVER is built for each of Canada's ten provinces for a one-year period based on 2018 data. The results are used to assess the system flexibility and transmission network sufficiency as well as operational aspects including costs and emissions. The results indicate the SILVER's improved hydro modelling enhances the result of operation model by 4% on average. Also, the results demonstrate high flexibility of Canada's hydro-dominated network side along with free capacity in the transmission network can realize 54% VREs penetration rate on average for VREs curtailment less than 10%. In addition, implementation of demand side programs improves the VREs penetration rate by 2%–6% on average.

Robust Flexible Unit Commitment in Network-Constrained Multicarrier Energy Systems

The coordinated operation of different energy systems, such as electrical, gas, and heating, can improve the efficiency of the whole energy system and facilitate the larger penetration of renewable energy resources in the electricity generation portfolio. However, appropriate models considering various technical constraints of the energy carriers (e.g., gas system pressure limit and heat losses in the district heating networks) are needed to effectively assess the true impact of integrated energy system (IES) operation on the overall system’s performance. This article proposes a flexible unit commitment (UC) problem for coordinated operation of electricity, natural gas, and district heating networks, called multicarrier network-constrained unit commitment (MNUC), to minimize the operation cost of the IES. Besides, an integrated demand response (IDR) program is considered as a promising solution to improve consumers’ electricity, gas, and heat consumption patterns and to increase the power dispatch of combined heat and power units. Multienergy storage systems are also included in the proposed model to decrease the impact of multienergy network constraints on the overall system’s performance. To model the uncertainties involved in the operation of the three networks, a combined robust/stochastic approach is preferred in the MNUC problem considering multicarrier energy storage systems and the IDR program. Numerical results show that the whole operation cost of the IES has decreased by 2.58% considering the IDR program and multienergy storage systems.

AC network-constrained unit commitment via conic relaxation and convex programming

To address the AC network-constrained unit commitment problem, we propose an effective three-step solution approach. We first solve a DC (mixed-integer linear) approximation of the original problem to identify an initial solution and the set of transmission lines likely to be congested. Secondly, with the linear solution as a starting point, we solve a mixed-integer second-order conic relaxation of the original problem using an active set strategy regarding transmission constraints. Finally, using the solution of the relaxed problem as a starting point, we solve a set of increasingly accurate continuous convex optimization problems to ensure full AC feasibility. We show the effectiveness of the proposed approach using the IEEE 24-bus test system and an Illinois 200-bus system with 49 generators and 245 lines.

A Distributionally Robust AC Network-Constrained Unit Commitment

This paper presents a distributionally robust network-constrained unit commitment (DR-NCUC) model considering AC network modeling and uncertainties of demands and renewable productions. The proposed model characterizes uncertain parameters using a data-driven ambiguity set constructed by training samples. The non-convex AC power flow equations are approximated by convex quadratic and McCormick relaxations. Since the proposed min-max-min DR-NCUC problem cannot be solved directly by available solvers, a new decomposition algorithm with proof of convergence is reported in this paper. The master problem of this algorithm is solved using both primal and dual cuts, while the max-min sub-problem is solved using the primal-dual hybrid gradient method, obviating the need for using duality theory. Also, an active set strategy is proposed to enhance the tractability of the decomposition algorithm by ignoring the subset of inactive constraints. The proposed model is applied to a 6-bus test system and the IEEE 118-bus test system under different conditions. These case studies illustrate the performance of the proposed DR-NCUC model to characterize uncertainties and the superiority of the proposed decomposition algorithm over other decomposition approaches using either primal or dual cuts.

Network-constrained unit commitment with piecewise linear AC power flow constraints

This paper presents a new network-constrained unit commitment (UC) model, which is formulated as a mixed-integer linear programming (MILP) optimization problem with embedded ac power flow (AC-PF) constraints. Piecewise linear AC-PF equations are formulated, which constrain the UC dispatch schedule within permissible transmission capacity and voltage deviation limits, solely by executing the main optimization problem. The proposed model is tested on a six-bus and the IEEE RTS-79 test systems, using GAMS software, whereas the obtained AC-PF results are validated against DIgSILENT/Powerfactory package. The UC schedules are comparatively assessed with detailed results presented in the relevant literature for the same test systems, employing different UC+AC-PF models.

Assessment of energy storage systems as a reserve provider in stochastic network constrained unit commitment

Assessment of energy storage systems as a reserve provider in stochastic network constrained unit commitment

Congestion management via increasing integration of electric and thermal energy infrastructures

Congestion caused in the electrical network due to renewable generation can be effectively managed by integrating electric and thermal infrastructures, the latter being represented by large scale District Heating (DH) networks, often fed by large combined heat and power (CHP) plants. The CHP plants could further improve the profit margin of district heating multi-utilities by selling electricity in the power market by adjusting the ratio between generated heat and power. The latter is possible only for certain CHP plants, which allow decoupling the two commodities generation, namely the ones provided by two independent variables (degrees-of-freedom) or by integrating them with thermal energy storage and Power-to-Heat (P2H) units. CHP units can, therefore, help in the congestion management of the electricity network. A detailed mixed-integer linear programming (MILP) optimization model is introduced for solving the network-constrained unit commitment of integrated electric and thermal infrastructures. The developed model contains a detailed characterization of the useful effects of CHP units, i.e., heat and power, as a function of one and two independent variables. A lossless DC flow approximation models the electricity transmission network. The district heating model includes the use of gas boilers, electric boilers, and thermal energy storage. The conducted studies on IEEE 24 bus system highlight the importance of a comprehensive analysis of multi-energy systems to harness the flexibility derived from the joint operation of electric and heat sectors and managing congestion in the electrical network.

A Fast Penalty-Based Gauss-Seidel Method for Stochastic Unit Commitment With Uncertain Load and Wind Generation

Stochastic network-constrained unit commitment (S-NCUC) can be used to manage the uncertainty of an increasing penetration level of renewable energy effectively. However, the drawbacks of the progressive hedging algorithm (PHA) based solutions are that they are not provably convergent due to the non-convexity of S-NCUC. Additionally, the solution obtained is usually fractional-valued (non-binary) and therefore not readily implementable. In this paper, we apply a novel Penalty-Based Gauss-Seidel (PBGS) algorithm in solving S-NCUC using an exact augmented Lagrangian representation with proven convergence. To improve the computational efficiency of the PBGS, we further propose an accelerating technique with rigorous proof to skip solving scenarios when certain conditions are met during iterations. We numerically validate the proposed algorithms on the IEEE 118-bus system and a practically-sized ERCOT-like system, both with variable wind generation. Numerical results demonstrate the efficacy of the proposed algorithms in yielding high-quality S-NCUC solutions. The merits of the proposed algorithms are also revealed in comparison with PHA and extensive-form (EF) based mixed-integer programming solutions.

Techno-economic evaluation of transportable battery energy storage in robust day-ahead scheduling of integrated power and railway transportation networks

In recent years, the use of renewable energy (RE) sources has an upward trend due to the environmental and economic reasons. However, finding a solution method to manage the fluctuating nature of these sources and more efficient utilization of total generation capacity are challenging problems, especially when there is a high penetration of REs in power systems. On the other side, the network congestion in power grids is another obstacle that inhibits the full utilization of REs. Battery-based energy storage transportation using a railway network leads to emerging high-efficiency technology called transportable battery-based energy storage (TBES) system. TBES technology is a practical and economical option to reduce transmission congestion and increase the utilization of the energy storage systems’ (ESSs’) capacity by providing additional facility to transfer power. As a flexible resource, TBES can adapt to the load profile of the system at peak-load hours and result in cost reduction and more prudent management of wind power variations. The demand response (DR) program is another solution to deal with wind power uncertainty and has a considerable impact on reducing power network congestion and total operation cost by peak-load shaving. Hence, to overcome the mentioned challenges and obstacles, this paper focuses on solving a robust network constrained unit commitment (NCUC) with TBES and DR programs. To manage the wind power uncertainty, an information gap decision theory (IGDT)-based robust optimization technique is proposed to obtain maximum robustness against the wind power uncertainty. The advantage of the presented model is that neither probability distribution functions (PDFs) nor scenario generation are required. The 6-bus power system coordinated with the 3-station railway network is applied as the test system. Numerical studies pointed out that integrating TBES technology in IGDT-based robust NCUC problems and considering the DR program has improved the power system’s flexibility and uncertainty management of wind power, alleviated the congestion, and reduced the optimized cost. Simulation results revealed a 6.5% cost reduction by applying TBES and also 11.3% cost decrement by developing coordinated TBES and DR.