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The unit commitment (UC) problem aims to find an optimal schedule of generating units subject to demand and operating constraints for an electricity grid. The majority of existing algorithms for the UC problem rely on solving a series of convex relaxations by means of branch-and-bound and cutting-planning methods. The objective of this paper is to obtain a convex model of polynomial size for practical instances of the UC problem. To this end, we develop a convex conic relaxation of the UC problem, referred to as a strengthened semidefinite program (SDP) relaxation. This approach is based on first deriving certain valid quadratic constraints and then relaxing them to linear matrix inequalities. These valid inequalities are obtained by the multiplication of the linear constraints of the UC problem, such as the flow constraints of two different lines. The performance of the proposed convex relaxation is evaluated on several hard instances of the UC problem. For most of the instances, globally optimal integer solutions are obtained by solving a single convex problem. For the cases where the strengthened SDP does not give rise to a global integer solution, we incorporate other valid inequalities. The major benefit of the proposed method compared to the existing techniques is threefold: (i) the proposed formulation is a single convex model with polynomial size and, hence, its global minimum can be found efficiently using well-established first-and second-order methods by starting from any arbitrary initial state, (ii) unlike heuristic methods and local-search algorithms that return local minima whose closeness to a global solution cannot be measured efficiently, the proposed formulation aims at obtaining global minima, (iii) the proposed convex model can be used in convex-hull pricing to minimize uplift payments made to generating units in energy markets. The proposed technique is extensively tested on IEEE 9-bus, IEEE 14-bus, IEEE 30-bus, IEEE 57-bus, IEEE 118-bus, and IEEE 300-bus systems with different settings and over various time horizons. [ABSTRACT FROM AUTHOR]

For the purposes of lowering environmental emission cost and increasing economic profit, energy efficient combined heat and power (CHP) units can be integrated with conventional separate heat and power production units to meet heat and power demands. The goal of this study is to develop and examine a novel heuristic and deterministic optimization algorithm for solving the profit-based unit commitment (PBUC) problem for a generation company with integrated CHP-thermal-heat only system for (i) satisfying demands for heat and power, (ii) selling spinning reserve for power, (iii) reducing environmental CO 2 emission cost, and (iv) accounting for valve-point effects for steam turbines. For validation, the developed optimization algorithm is applied to power systems examined in other studies for a 24 h operation period, where reduction of 2.54–5.86% in environmental CO 2 emission and improvements of 2.91–5.67% in economic profit are achieved. When environmental CO 2 emission cost and valve-point effects are considered, the utilization of CHP units in an integrated CHP-thermal-heat only system results in environmental CO 2 emission reduction and economic profit improvement by and 29.45 and 15.41%, respectively, as compared with those resulting from operating thermal and heat only units separately. [ABSTRACT FROM AUTHOR]

This paper has proposed a novel Hybrid modified Genetic – Imperialist Competitive Algorithm (HGICA) for solving thermal Unit Commitment Problem (UCP). The UCP is a mixed integer problem with many equality and inequality constraints like the minimum down and minimum up time, spinning reserve, and ramp rate so need to a complex optimization process. In this paper the constraint handling of the problem is realized without any penalizing of solutions so a wide range of feasible solutions will be available for final optimum response. The proposed modified genetic is a novel method which has better performance than its original version and helps ICA to find more optimized responses and escape for local minimum areas easily. The main advantages of HGICA are good quality of the solution and high computational speed, which make it a suitable method for solving optimization problems. This method is carried out for three case studies including 10 and 20 units systems to efficiency of it be proved. Also the obtained results is compared to other optimization methods represented in literature for different scenarios. [ABSTRACT FROM AUTHOR]

Wind power probabilistic forecast is being used as input in several decision-making problems, such as stochastic unit commitment, operating reserve setting and electricity market bidding. This work introduces a new on-line quantile regression model based on the Reproducing Kernel Hilbert Space (RKHS) framework. Its application to the field of wind power forecasting involves a discussion on the choice of the bias term of the quantile models, and the consideration of the operational framework in order to mimic real conditions. Benchmark against linear and splines quantile regression models was performed for a real case study during a 18 months period. Model parameter selection was based on k -fold crossvalidation. Results showed a noticeable improvement in terms of calibration, a key criterion for the wind power industry. Modest improvements in terms of Continuous Ranked Probability Score (CRPS) were also observed for prediction horizons between 6 and 20 h ahead. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm to deal with thermal Unit Commitment which takes into account the intermittency and volatility of the renewable energies such as wind and solar energies. Dynamic Programming (DP) integrating Priority Listing order (PL) based on Best Per Unit Cost (BP) was applied to commit the thermal units in an isolated island with generators based on renewable sources. In this work, the effects of a high time resolutions such as 60, 30, 15, 10 and 5 min on production costs, reserves and intermittent generators are investigated. In order to demonstrate the capability of the proposed algorithm, two cases were studied. Firstly, a test system composed of ten diesel generators, three wind turbines and one Photovoltaic (PV) power plant is examined and then the IEEE 118-bus test system, integrating wind and PV power plants, is considered. The presented simulation results show that a proper schedule for each generation unit can be reached at a time resolution closer to real time unit commitment and economic dispatch while a high level of reliability can be guaranteed by assuring practical constraints fulfillment. [ABSTRACT FROM AUTHOR]

Hydro-thermal unit commitment (HTUC) is an extension of unit commitment (UC) problems. The hydro-thermal unit commitment problem considered in this study aims at minimizing the total fuel cost of thermal units while satisfying the constraints of spinning reserve, minimum online/offline, ramp rate, hydraulic networks, etc. A hybrid particle swarm optimization approach with small population size (HPSO-SP) is presented for solving the optimal short-term HTUC problem. In the proposed approach, three extra handling operations, i.e. mutation, DE-acceleration, and migration have been proposed for both binary and continuous variables to ensure the effects of small population. A repair strategy to the main equality and inequality constraints has also been employed to improve the searching efficiency of the algorithm. Several well-known UC test systems in literature are considered to test the proposed HPSO-SP approach first. After verification on UC problems, this approach is applied to solve several HTUC test systems and a practical hydro-thermal system in China. The final results show the feasibility and effectiveness of the HPSO-SP approach. [ABSTRACT FROM AUTHOR]

The utilization of energy efficient CHP (combined heat and power) systems provides opportunities for GENCOs (generation companies) to simultaneously increase their economic benefits and decrease environmental emissions. The goals of this study are to develop and simulate a novel approach for optimal economic dispatch scheduling for a GENCO to maximize economic profit and minimize environmental emissions based on integration of CHP systems with conventional TG (thermal power generating) units, where a DBD (double Benders decomposition) solution approach is proposed for optimization. The CHP PBUC (profit based unit commitment) problem with non-convex constraints for integration of CHP units into GENCOs generation portfolio is solved and, the results for several cases along with sensitivity analyses are reported. The results from DBD approach for optimization are validated based on comparison with those from gravitational search and imperialistic competitive algorithms as well as other algorithms examined in other studies. When CHP units are integrated with a 10-TG unit system, results from implementing the proposed DBD approach are extremely encouraging, as GENCO profit for providing electricity and heat energy is increased by 518.78% and environmental emissions are reduced by 7.57%, in comparison with case of GENCO using TG units for meeting the same electricity demand. [ABSTRACT FROM AUTHOR]

This paper is the first of a two part study that considers long term, least cost, GHG (greenhouse gas) abatement pathways for an electricity system. Part 1 formulates a planning model to optimise these pathways and presents results for a single reference scenario. Part 2 applies this model to different scenarios and considers the policy implications. The planning model formulated has several constraints which are important when considering GHG abatement and widespread uptake of intermittent renewable generation. These constraints do not appear to have been integrated into a single planning model previously, and include constraints on annual GHG emissions, unit commitment, storage, plant dynamics and intermittent renewable generation. The model prioritises overall abatement, and therefore does not include a price on carbon or support for any particular technology. The model is applied to Australia's NEM (National Electricity Market) as an example. All model inputs – for technologies, demand, and meteorological data – are from the most current and authoritative public sources. As such, the results are transparently derived and both policy and technology neutral. For the reference scenario presented here, key technologies are wind from 2015, gas generation from 2030, and solar generation from 2040. [ABSTRACT FROM AUTHOR]

Short-term optimization models, usually applied to traditional problems like UC (unit commitment) and economic dispatch problem, are essential tools for the planning and operation of power systems. However, the large number of variables and restrictions, necessary for a good and more accurate representation of any electricity system, require high computational resources, frequently resulting in high computation times. This study proposes a simplified approach of a model for the electricity planning of power plants allocation based on the available resources. The model resources to quadratic penalty functions and avoid on/off binary variables. The approach is then supported on a non-linear optimization model able to solve this electricity planning problem in shorter computation times, with solutions close to the ones obtained with more complex models. The model is fully described and tested under different scenarios of an electricity system comprising thermal, wind, and hydropower plants. The results were compared to the ones obtained with a more complex model, analysing the main differences obtained for cost, CO 2 emissions and of wind power impacts on this electricity system. The most remarkable advantage of the simplified model comes from the significant reduction on computational time needed for state-of-the-art optimization solvers to provide an optimal solution, comparatively to mixed integer models. [ABSTRACT FROM AUTHOR]

To meet the increasingly high requirement of smart grid operations, considering AC power flow constraints in the NCUC (network-constrained unit commitment) is of great significance in terms of both security and economy. This paper proposes a decomposition method to solve NCUC with AC power flow constraints. With conic approximations of the AC power flow equations, the master problem is formulated as a MISOCP (mixed integer second-order cone programming) model. The key advantage of this model is that the active power and reactive power are co-optimised, and the transmission losses are considered. With the AC optimal power flow model, the AC feasibility of the UC result of the master problem is checked in subproblems. If infeasibility is detected, feedback constraints are generated based on the sensitivity of bus voltages to a change in the unit reactive power generation. They are then introduced into the master problem in the next iteration until all AC violations are eliminated. A 6-bus system, a modified IEEE 30-bus system and the IEEE 118-bus system are used to validate the performance of the proposed method, which provides a satisfactory solution with approximately 44-fold greater computational efficiency. [ABSTRACT FROM AUTHOR]

For many years, the UC (unit commitment) problem has been solved by complex numerical techniques or intelligent search algorithms, due to nonlinear and complex constraints. Many of the applied algorithms employ random searches, which leads to production of different solutions in different program runs. Priority list-based methods are a way out to this, as they produce robust results during a non-iterative procedure, and without help of trial and error efforts. Nevertheless, they have all proven inefficient. This paper introduces a new approach that generates the solutions using algorithm-specific constraint handling techniques, based on the priority list concept. The solution-making stages include: 1. Minimum up/down time establishment using a probabilistic priority list-oriented selection mechanism, 2. Spinning reserve constraint handling through a deterministic priority list-based process, 3. Power balance handling and a ramp rate modification procedure for generating efficient ramp-constrained solutions. The different steps are designed such that efficient modifications are applied in each step without violating the previously established constraints. Simulation results on different test systems reveal that the approach obtains robust and competitive results. A new 140-unit large-scale test system based on Korean power system is also presented for verifying applicability of the proposed approach on real world power systems. [ABSTRACT FROM AUTHOR]

Abstract: Fully EVs (electric vehicles) and PHEVs (plug-in hybrid electric vehicles) have attracted much attention in recent years. Towards an increasing share of EVs, their economic feasibility and impact on the electricity distribution have been studied in detail. However, little has been achieved in investigating the impact on the electricity generation systems. This paper presents a MILP (mixed-integer linear programming) unit commitment model with focus on the effect of EVs on the generation side. The most important advantage of the proposed method is the ability to solve systems with a very large number of EVs. The algorithm is demonstrated on a benchmark system, which has been widely used in the literature and has been used here for all scenarios. It is demonstrated that optimized charging (centrally controlled) is cheaper and allows for higher EV penetration, compared to random charging. Simulations were also run for two scenarios based on the advancement in the charging infrastructure: (1) perfect infrastructure, with opportunity for charging everywhere and (2) moderate infrastructure, where charging is possible only at the owners' homes. In both cases the generation cost increases by 1% for every 10% of additional EV penetration, the modest infrastructure case being slightly more expensive. [Copyright &y& Elsevier]

Abstract: Recently in electricity markets, a massive focus has been made on setting up opportunities for participating demand side. Such opportunities, also known as demand response (DR) options, are triggered by either a grid reliability problem or high electricity prices. Two important challenges that market operators are facing are appropriate designing and reasonable pricing of DR options. In this paper, time-of-use program (TOU) as a prevalent time-varying program is modeled linearly based on own and cross elasticity definition. In order to decide on TOU rates, a stochastic model is proposed in which the optimum TOU rates are determined based on grid reliability index set by the operator. Expected Load Not Supplied (ELNS) is used to evaluate reliability of the power system in each hour. The proposed stochastic model is formulated as a two-stage stochastic mixed-integer linear programming (SMILP) problem and solved using CPLEX solver. The validity of the method is tested over the IEEE 24-bus test system. In this regard, the impact of the proposed pricing method on system load profile; operational costs and required capacity of up- and down-spinning reserve as well as improvement of load factor is demonstrated. Also the sensitivity of the results to elasticity coefficients is investigated. [Copyright &y& Elsevier]