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Abstract Historically, energy system tools were predominantly proprietary and not shared with others. In recent years, there has been an increase in developing open source tools by international research and development organizations. More than half of the open energy modeling (openmod) initiative listed tools are based on the freely available scripting language Python. Previous comparisons of energy and power system modeling tools focused on comparisons such as which tool category (e.g. optimization, simulation) or energy demand (e.g. electricity, cooling, and heating) can be considered. Until now, the assessment of incorporated functions such as unit commitment (UC) or optimum power flow (OPF) has been ignored. Therefore, this work assesses 31 mostly open source tools based on 81 functions for their maturity. The result shows that available open source tools such as Switch, TEMOA, OSeMOSYS, and pyPSA are mature enough based on a function comparison with commercial or proprietary tools for serious use. Nevertheless, future commercial, as well as open source energy system analysis tools, have to consider more functions such as the impact of ambient air conditions and part-load behavior to allow better assessments of including high shares or renewable energy sources and other flexibility measures in existing and new energy systems. Highlights • overview about history within Open Source energy model development; • assessment of 81 modeling details; • detailed analysis of 31 energy models (mainly open source); • identifies area of further research requirements; • supports to achieve an unbiased decision process to select the 'best' suitable model; [ABSTRACT FROM AUTHOR]

As the level of uncertain renewable capacity increases on power systems worldwide, industrial and academic researchers alike are seeking a scalable, transparent, and effective approach to unit commitment under uncertainty. This paper presents a statistical ranking methodology that allows adaptive robust stochastic unit commitment using a modular structure, with much-needed flexibility. Specifically, this paper describes a bus ranking methodology that identifies the most critical buses based on a worst-case metric. An important innovation is the ability to identify alternative metrics on which to rank the uncertainty set—for example, to minimize economic dispatch cost or ramping needs, to provide a customized robust unit commitment solution. Compared to traditional robust unit commitment models, the proposed model combines statistical tools with analytical framework of power system networks. The resulting formulation is easily implementable and customizable to the needs of the system operator. The method and its applications are validated against other established approaches, showing equivalent solution to the state-of-the-art approach. Case studies were conducted on the IEEE-30, IEEE-118, and the pegase-1354 networks. In addition, the flexibility of bus ranking formulation is illustrated through implementation of alternative definitions of worst-case metrics. Results show that the bus ranking method performs as well as the best of these methods, with the provision of additional flexibility and potential for parallelization. [ABSTRACT FROM AUTHOR]

This paper proposes an integrated framework for operational flexibility assessment in power system planning with a significant share of intermittent renewable energy sources (RES). The framework proposed includes: (i) the formulation of an integrated generation expansion planning (GEP) and unit commitment (UC) model accounting for key short-term technical constraints, (ii) the elaboration of accurate and systematic horizon reduction methods to alleviate the computational burden of the resulting large-sized optimization problems and (iii) the definition of suitable metrics for the operational flexibility assessment of the obtained plans. The framework is applied to a ten year planning horizon of a realistically sized case study representing the national power system of France, under several scenarios of RES penetration levels and carbon limits, spanning levels of up to 50%. The importance of incorporating the detailed short-term constraints within long-term planning models is shown. The results of the assessment show that, under high renewable energy penetration, neglecting the short-term constraints may lead to plans significantly short on flexibility, reaching shortage levels of up to 50% in frequency and several GWs in magnitude. Also, the load not served reaches levels of up to 3% and carbon emission is underestimated by up to 60%. Furthermore, the results highlight the importance of relying on suitable quantitative metrics for operational flexibility assessment in power systems planning rather than solely relying on generic performance measures, such as system costs and mixes of power plants, which are shown not to sufficiently reflect the flexibility levels of the obtained plans. [ABSTRACT FROM AUTHOR]

This paper proposes a data-driven affinely adjustable distributionally robust method for unit commitment considering uncertain load and renewable generation forecasting errors. The proposed formulation minimizes expected total operation costs, including the costs of generation, reserve, wind curtailment, and load shedding, while guaranteeing the system security. Without any presumption about the probability distribution of the uncertainties, the proposed method constructs an ambiguity set of distributions using historical data and immunizes the operation strategies against the worst case distribution in the ambiguity set. The more historical data is available, the smaller the ambiguity set is and the less conservative the solution is. The formulation is finally cast into a mixed integer linear programming whose scale remains unchanged as the amount of historical data increases. Numerical results and Monte Carlo simulations on the 118- and 1888-bus systems demonstrate the favorable features of the proposed method. [ABSTRACT FROM AUTHOR]

In Part II of this paper, we present formulations for three two-settlement market models: Baseline cost-minimization (\textitStoch- \textitOpt); and two sequential market models in which an independent system operator (ISO) runs real-time (RT) balancing markets after making day-ahead (DA) generating unit commitment decisions based upon deterministic wind forecasts, while virtual bidders arbitrage the two markets ( ${{\textit{Seq}}}$ and ${{\textit{Seq}}}$- ${{SS}}$). The latter two models differ in terms of whether some slow-start generators can self-schedule in the DA market while anticipating probabilities of RT prices. Models in ${{\textit{Seq}}}$ and ${{\textit{Seq}}}$- ${{SS}}$ build on components of the two-settlement equilibrium model ( \textitStoch- ${{MP}}$) defined in Part I of this paper [J. Kazempour and B. F. Hobbs, “Value of flexible resources, virtual bidding, and self-scheduling in two-settlement electricity markets with wind generation - Part I: Principles and competitive model,” IEEE Trans. Power Syst., vol. 33, no. 1, pp. 749–759, Jan. 2018]. We then provide numerical results for all four models. A simple single-node case illustrates the economic impacts of flexibility, virtual bidding, and self-schedules, and is followed by a larger case study based on the 24-node IEEE reliability test system. Their results confirm that flexible resources, including fast-start generators and demand response, can reduce expected costs in a sequential two-settlement market. In addition, virtual bidders can also improve the functioning of sequential markets. In some circumstances, virtual bidders (together with self-scheduling by slow-start generators) enable deterministic ISO DA markets to obtain the least (expected) cost unit commitments. [ABSTRACT FROM AUTHOR]

This paper provides a reformulation of the scenario-based two-stage unit commitment problem under uncertainty that allows finding unit-commitment plans that perform reasonably well both in expectation and for the worst case. The proposed reformulation is based on partitioning the sample space of the uncertain factors by clustering the scenarios that approximate their probability distributions. The degree of conservatism of the resulting unit-commitment plan (that is, how close it is to the one provided by a purely robust or stochastic unit-commitment formulation) is controlled by the number of partitions into which the said sample space is split. To efficiently solve the proposed reformulation of the unit-commitment problem under uncertainty, we develop two alternative parallelization and decomposition schemes that rely on a column-and-constraint generation procedure. Finally, we analyze the quality of the solutions provided by this reformulation for a case study based on the IEEE 14-node power system and test the effectiveness of the proposed parallelization and decomposition solution approaches on the larger IEEE 3-Area RTS-96 power system. [ABSTRACT FROM PUBLISHER]

In this work, we develop a mathematical model and framework to represent rolling-horizon unit commitment (UC) processes with multiple periodicities. In control center operations, UC is solved repeatedly to adjust device commands based on new information about load, generation availability, renewable energy production, and other aspects of system state as uncertain conditions are realized. We develop a three-level model including 24-h UC, rolling-horizon UC (RHUC), and economic dispatch (ED) and give formulations for the three problems including interdependencies. This framework allows for evaluation of, among other things, different periodicities of the rolling horizon problem and the benefits of more accurate forecasting information. Experimental results are shown for a 6-bus system and a 3012-bus system with wind generation in two wind scenarios. Although the generation costs are very similar, the deviation between RHUC schedules and actual deployment is noted to be superior for a 20-min periodicity compared to a 30-min periodicity. Additionally, less reserve is deployed in the 20-min RHUC case. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of plug-in electric vehicle (EV) integration on the power systems scheduling and energy cost. An intermediary entity, the EV aggregator, participates in the market on behalf of the EV owners by optimally self-scheduling under the price-taking approach. Through detailed rolling simulations for a year and different EVs’ penetration scenarios at a large insular power system, this work highlights the different impact of direct and smart charging on power system scheduling and energy costs, the limitations of the price-taking approach, which is widely used in self-scheduling models, and the difference in system value and market value that smart charging adoption creates in restructured markets under the marginal pricing rule. [ABSTRACT FROM AUTHOR]

We propose a two-stage mixed-integer linear stochastic optimization model to analyze the scheduling of electricity-production units under natural gas-supply uncertainty due to pipeline congestion and natural gas-price variability. The first stage of this stochastic optimization model represents the day-ahead scheduling (i .e., unit commitment) stage, while the second stage represents actual real-time operations through a number of scenarios. We use this model to analyze the effect on unit commitment and dispatch of two types of natural gas-supply conditions. First, we analyze a case involving low-cost natural gas supply with natural gas-transmission issues related to potential gas-pipeline congestion. We then examine a case involving higher-cost natural gas, which is used solely to attain feasibility with fast-ramping events. The first case mimics situations in the ISO New England system, in which relatively low-cost natural gas supply is uncertain in cold-weather conditions due to natural gas-transmission bottlenecks. The second case is reminiscent of situations in the California ISO system, in which relatively expensive but flexible natural gas-fired units need to be used to handle rapid changes in net demand in the early mornings and late afternoons. [ABSTRACT FROM AUTHOR]

In the process of scheduling multiple generating units, an up/down decision for every generating unit has to be made for every hour on the planning horizon. Once the unit commitment is decided for every generating unit, a generation level (economic dispatch) from the committed unit is calculated to minimize the total operation cost. Such a problem is known as a unit commitment problem (UCP). Because an up/down pattern is expressed with a vector of binary variables, one has to solve a combinatorial optimization problem. Due to electricity liberalization or the introduction of distributed power sources in recent years, the need to solve the UCP by autonomous distributed agents, that is, solving distributed UCP (DUCP), comes about. This paper proposes a method of solving the DUCP with a Walrasian auction. The main feature of the proposed method is that not only a generation-level pattern but also an up/down pattern can be determined. The results of computational experiments show that the DUCP is solved through the cooperation of autonomous distributed agents by using the proposed method. [ABSTRACT FROM PUBLISHER]

Reserve requirements promote reliability by ensuring resources are available to rebalance the power system following random disturbances. However, reliability is not guaranteed when dispatch is limited by transmission constraints. In this work, we propose a modified form of reserve requirement that identifies response sets for distinct contingency scenarios. The approach disqualifies reserve from counting towards a particular scenario if transmission constraints are likely to render that reserve undeliverable. A decomposition algorithm for security-constrained unit commitment dynamically updates the response sets to address changing conditions. Testing on the RTS 96 test case demonstrates the approach applied in tandem with existing reserve policies to avoid situations where reserve is not deliverable due to transmission constraints. Operational implications of the proposed method are discussed. [ABSTRACT FROM PUBLISHER]

To handle significant variability in loads, renewable energy generation, as well as various contingencies, (two-stage) robust optimization method has been adopted to construct unit commitment models and to ensure reliable solutions. In this paper, we further explore and extend the modeling capacity of two-stage robust optimization and present two new robust unit commitment variants: the expanded robust unit commitment and the risk constrained robust unit commitment model. We derive structural properties, demonstrate the connection to the popular scenario based stochastic unit commitment models, and present a customized column-and-constraint generation method. Numerical experiments on those models are performed using practical data sets, which illustrate their modeling strength, economic outcomes, and the algorithm performance in solving those models. [ABSTRACT FROM PUBLISHER]

Unit commitment (UC) problem is one of the medium term decision problems in power system and it is used to determine the mix/ combination of generators to be operated or to be idle. By solving the UC problem, once the binary data is obtained the next level is to find the optimum level of dispatch of each generators in the mix. For a large generating system UC program is a large scale, non linear mixed integer programming (MIP). This paper proposes a hybrid algorithm to solve UC problem by considering equality and inequality constraints. The objective of the algorithm is to minimize the fuel cost after meeting all the possible constraints. The proposed algorithm initially solved the UC problem by modified LR method. LR method most often gives sub optimal solution, so once the problem is solved by LR and if some of the output is infeasible then by solving it by heuristic technique such as priority list method, we can modify LR. The generation output level is determined by particle swarm optimization method. The algorithm was tested in four unit eight hour systems and ten unit twenty four hour system. The simulation output shows that the developed algorithm performed well and also applicable for large systems. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this paper is to introduce a semidefinite programming relaxation based technique combined with selective pruning (SDPSP) to achieve faster convergence to a (near)-optimal solution of the unit commitment problem. The compact form of the SDP variable matrix and the concise formulation of the start-up cost constraints contribute to a reduced constraint framework dimension which has profound implications on performance. The computation time is also significantly enhanced by the selective pruning algorithm that prunes all the feasible periods and effectively shrinks the scheduling horizon as the solution is approached. The selective pruning algorithm incorporates two complementary repair mechanisms that exploit the characteristics of the constraint formulation and the properties of SDP relaxation to correct the binary variable violations. The method efficiently handles inter-temporal constraints such as ramp rates that are deemed crucial in practical systems. The prowess of the method is demonstrated by comparing it with other recently proposed techniques. [Copyright &y& Elsevier]

This paper examines the polytope of feasible power generation schedules in the unit commitment (UC) problem. We provide computational results comparing formulations for the UC problem commonly found in the literature. We introduce a new class of inequalities, giving a tighter description of feasible operating schedules for generators. Computational results show that these inequalities can significantly reduce overall solution times. [ABSTRACT FROM PUBLISHER]

In this paper, we present a unit commitment problem with uncertain wind power output. The problem is formulated as a chance-constrained two-stage (CCTS) stochastic program. Our model ensures that, with high probability, a large portion of the wind power output at each operating hour will be utilized. The proposed model includes both the two-stage stochastic program and the chance-constrained stochastic program features. These types of problems are challenging and have never been studied together before, even though the algorithms for the two-stage stochastic program and the chance-constrained stochastic program have been recently developed separately. In this paper, a combined sample average approximation (SAA) algorithm is developed to solve the model effectively. The convergence property and the solution validation process of our proposed combined SAA algorithm is discussed and presented in the paper. Finally, computational results indicate that increasing the utilization of wind power output might increase the total power generation cost, and our experiments also verify that the proposed algorithm can solve large-scale power grid optimization problems. [ABSTRACT FROM PUBLISHER]

This letter introduces a deterministic global optimization methods for unit commitment (UC) problem based on outer approximation method (OAM). The proposed Multi-cuts OAM (MCs-OAM) decomposes the UC problem into a mixed integer linear programming (MILP) master problem and several nonlinear programming (NLP) subproblems, whereas only one NLP in classic OAM. After elaborately designing the terminating criterion for solving the bigger but tighter MILP master problems, MCs-OAM can obtained higher quality solutions with fewer main iterations and less total CPU times, although solving more NLPs consumes more CPU times than OAM. The numerical results on 42 test systems of up to 200 units show that the MCs-OAM is very promising for large scale UC problems because that it can obtain high-quality solutions in reasonable time. [ABSTRACT FROM AUTHOR]

This paper presents an online economic re-dispatch scheme based on the generation cost optimization with security constraints to mitigate line overloads before and after line and generation contingencies. The proposed optimization model considers simplification of mathematical expressions calculated from online variables as the power transfer distribution factor (PTDF) and line outage distribution factor (LODF). Thus, a first algorithm that calculates economic re-dispatch for online operation to avoid overloads during the normal operation and a second algorithm that calculates online emergency economic re-dispatch when overloads occur due to line and generator contingencies are proposed in this paper. The results show that the proposed algorithms avoid overload before and after contingencies, improving power system security, and at the same time reducing operational costs. This scheme allows a reduction of power generation units in the electricity market during online operation that considers line overloads in the power system. [ABSTRACT FROM AUTHOR]