Unit commitment for power plants with tracking speed limits

Researches on the unit commitment with transmission network have been reported recently. However, most of these researches mainly discussed the security constrained unit commitment, while the relationship between unit commitment and transmission losses was not considered. However, from the standpoint of operating reserve for ensuring power supply reliability, a unit commitment considering transmission losses is required. Further, under the deregulation and liberalization of the electric power industry, not only the line's security but also transmission losses are expected to play an important role in calculating the network access charge, and unit commitment taking into account transmission losses is also desired from this viewpoint. In this paper, a unit commitment approach with both transmission losses and line flow constraint is presented. Based on a heuristic iterative optimization method, first, an initial schedule is created by using a successively decommitting unit approach that is proposed in this paper. Then, we determine constraints included in the unit commitment schedule by a heuristic iterative optimization approach, in which an algorithm able to get rid of line overload by DC optimal power flow is developed. Through numerical simulations on two test power systems, the effectiveness of the proposed method is shown. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 142(4): 9–19, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10116

This paper presents the solving unit commitment (UC) problem using Modified Subgradient Method (MSG) method combined with Simulated Annealing (SA) algorithm. UC problem is one of the important power system engineering hard-solving problems. The Lagrangian relaxation (LR) based methods are commonly used to solve the UC problem. The main disadvantage of this group of methods is the difference between the dual and the primal solution which gives some significant problems on the quality of the feasible solution. In this paper, MSG method which does not require any convexity and differentiability assumptions is used for solving the UC problem. MSG method depending on the initial value reaches zero duality gap. SA algorithm is used in order to assign the appropriate initial value for MSG method. The major advantage of the proposed approach is that it guarantees the zero duality gap independently from the size of the problem. In order to show the advantages of this proposed approach, the four-unit Tuncbilek thermal plant and ten-unit thermal plant which is usually used in literature are chosen as test systems. Penalty function (PF) method is also used to compare with our proposed method in terms of total cost and UC schedule.

The authors present a thermal unit commitment method for interconnected multiarea power systems. The method is a natural extension of the commitment utilization factor (CUF)-based single-area unit commitment method. CUF is extended to reflect the impact of multiarea transmission interconnection constraints on the utilization efficiency of committing a candidate unit at a given priority position. The multiarea CUF is used in conjunction with the average full load cost. (AFLC) to efficiently determine the optimal or a near-optimal multiarea priority commitment order. This priority order is then used to generate the multiarea unit commitment schedule. The excellent quality of the results of the CUF-based multiarea method is consistent with that of the previously reported single-area method.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The principle of power determined by heat and the anti-peak-regulation characteristics of wind power output cause the difficulty of peak regulation in the power grid along with the production of the abandon wind. By analyzing the topological differences between power system and thermal system, establishing positive and negative spinning reserve constraints of the district heating network, and considering both the hot and the cold start-up cost of the units as well as the thermoelectric coupling of the CHP units, a unit commitment model of wind power accommodation in combined heat and power system— whose objective function is to minimize the total coal consumption and environmental cost of the units—is presented in this paper. In this model, unit commitment and optimized dispatch are combined, the electric load is shared by all the units, and the heat load is balanced by the local CHP plants respectively. Finally, the optimization results show that while meeting power and heat load demand, the model of this paper can timely start up or shut down units according to load change and spinning reserve demand, reduce total minimum technical output of operating units, and further improve wind power accommodation rate.

This paper presents a trustworthy unit commitment study to schedule both Renewable Energy Resources (RERs) with conventional power plants to potentially decarbonize the electrical network. The study has employed a system with three IEEE thermal (coal-fired) power plants as dispatchable distributed generators, one wind plant, one solar plant as stochastic distributed generators, and Plug-in Electric Vehicles (PEVs) which can work either loads or generators based on their charging schedule. This paper investigates the unit commitment scheduling objective to minimize the Combined Economic Emission Dispatch (CEED). To reduce combined emission costs, integrating more renewable energy resources (RER) and PEVs, there is an essential need to decarbonize the existing system. Decarbonizing the system means reducing the percentage of CO2 emissions. The uncertain behavior of wind and solar energies causes imbalance penalty costs. PEVs are proposed to overcome the intermittent nature of wind and solar energies. It is important to optimally integrate and schedule stochastic resources including the wind and solar energies, and PEVs charge and discharge processes with dispatched resources; the three IEEE thermal (coal-fired) power plants. The Water Cycle Optimization Algorithm (WCOA) is an efficient and intelligent meta-heuristic technique employed to solve the economically emission dispatch problem for both scheduling dispatchable and stochastic resources. The goal of this study is to obtain the solution for unit commitment to minimize the combined cost function including CO2 emission costs applying the Water Cycle Optimization Algorithm (WCOA). To validate the WCOA technique, the results are compared with the results obtained from applying the Dynamic Programming (DP) algorithm, which is considered as a conventional numerical technique, and with the Genetic Algorithm (GA) as a meta-heuristic technique.

Unit commitment (UC) is the process of committing the most optimal combination of generators in accordance with the power system security, load and cost. This paper proposes solution of UC problem using Forward Dynamic Programming method. The results are generated on two test systems and compared with that of the new and emerging techniques. Day ahead scheduling is done for performing the unit commitment as it is practically realizable and is the most common approach for performing UC. For this, the forecasted load demand of the day ahead is considered. The forecasted data is necessary to be considered for pre-planning the UC so that the units which may not be necessary to operate, can be shut down. Accordingly, the strategies can be developed for any real time discrepancies which may arise in the system network like load increase or decrease, faults etc. All the operational and technical constraints are taken care of during simulation. A spinning reserve is also considered to cater sudden and unexpected load increase. The simulation results obtained on two test systems are analysed and presented.

As an attempt to reduce reliance on fossil fuels to satisfy electricity demand, the penetration of renewable sources like wind or solar power has experienced rapid growth in recent years. Due to their intermittent nature, the exact contribution of renewable sources to electricity supply is at least partly unknown. In order to be able to accommodate this uncertainty in real-time, sufficient capacities in the form of thermal and hydro power plants must be available. On that account, power system operators solve the so-called unit commitment problem after bidding in the day-ahead market closes to derive schedules for their power plants which can ensure system reliability at low costs. In literature, two approaches have mainly been studied in this context: Robust and stochastic unit commitment. Being a worst-case formulation, robust unit commitment puts its focus on reliability. A common drawback of this approach is that it tends to deliver over-conservative schedules. Stochastic unit commitment on the other hand creates more cost-effective schedules by preparing for the expected case, but either fails to guarantee system reliability or puts a high workload on the CPU. The limitations of known formulations have sparked interest in so-called hybrid approaches, which aim at combining the ideas of robust and stochastic unit commitment in a favorable way. This paper reports a novel hybrid approach to solve the unit commitment under uncertainty, which yields both robust and cost-efficient schedules. The new method respects the continuous nature of uncertainties and is thus in particular favorable for applications in power systems with high penetration of volatile renewable sources. By merging the ideas of robust and stochastic unit commitment, the proposed hybrid formulation minimizes the expected worst-case dispatch costs. Our method relies on partitioning the continuous range of the uncertainties into subsets. By means of the number of partitions, the solution can be adjusted between the conservative robust and the cost-efficient stochastic unit commitment in a user-friendly manner. A Benders decomposition algorithm is derived to solve the hybrid unit commitment efficiently. Finally, a case study confirms the superior performance of the proposed method.

Unit commitment is becoming a complex problem with the increasing constraints due to the restructuring of power system and the escalation in inclusion of various types of Distributed Generation sources. These sources offer a significantly lower generation compared to the conventional sources. Also they pose synchronising problems with the grid since the electricity cannot be transmitted over long distances and thus they provide the localized consumption of energy. In this paper, unit commitment is performed with optimal spinning reserve allocation and the assessment of reliability in terms of loss of load, in the presence of solar integration into the power system. The ‘Loss Of Load Probability’ (LOLP) index is utilized for determining the level of reliability of the obtained results. The Spinning Reserve (SR) considered in the UC calculations, is a constant value and it is not varied with respect to the changes in solar generation. Here, the spinning reserve optimality is determined with respect to the changes in power injection due to the solar energy integration into the power system. Dynamic programming technique is applied on two systems (four generator and ten generator systems) and the results are compared with those obtained without the consideration of LOLP, SR optimality and solar energy sources.

Unit commitment is a complex problem which, until now and for real size systems, has been solved using heuristic methods only. This paper proposes a new Lagragian relaxation method, which produces ??-optimal solutions. The flexibility of the algorithm is such that numerous developments can be envisaged, such as simultaneous management of pumping units, probabilistic determination of the spinning reserve, etc.

A heuristic solution for short-term thermal unit commitment is presented. The algorithm has been developed as a FORTRAN-based production-type program. Tests of this algorithm on data provided by a midwest utility have demonstrated satisfactory results. The proposed algorithm produces the same unit commitment schedule as a standard dynamic programming (DP)-based algorithm in one fourth the computation time for the DP-based algorithm. The algorithm is nearly linear with the number of unit periods. A unit period is defined as the online duration time versus operation level for each unit. Operating system restrictions limit the prototype to 30 thermal power units as implemented on a PS/2 model 50 personal computer.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The fast-increasing introduction of renewable energy sources (RESes) leads to some problems in electrical power network due to fluctuating generated power. A power system must be operated with provision of various reserve powers like governor free capacity, load frequency control and spinning reserve. Therefore, the generator’s schedule (unit commitment schedule) should include the consideration of the various power reserves. In addition, it is necessary to calculate the annual operational costs of electric power systems by solving the unit commitment per week of thermal power generators and pumped storages in order to compare and examine the variance of the operational costs and the operating ratio of the generators throughout the year. This study proposes a novel annual analysis for the thermal power generator and pumped storages under a massive introduction of RESes. A weekly unit commitment schedule (start/stop planning) for thermal power generator and pumped storages has been modeled and calculated for one year evaluation. To solve the generator start/stop planning problem, Tabu search and interior point methods are adopted to solve the operation planning for thermal power generators and the output decision for pumped storages, respectively. It is demonstrated that the proposed method can analyze a one-year evaluation within practical time. In addition, by assuming load frequency control (LFC) constraints to cope with photovoltaic (PV) output fluctuations, the impact of the intensity of LFC constraints on the operational cost of the thermal power generator has been elucidated. The increment of the operational cost of the power supply with increasing PV introduction amount has been shown in concrete terms.

The information needed for fundamental modelling of the power markets -- the efficiency, start-up, fixed, and variable operating costs of each power plant -- is not publicly available. These parameters are usually estimated by considering the type of technology and the age of a power plant. We present a method to extract these parameters for thermal power plants on the British electricity market using only the publicly available data. For each power plant, we solve a bilevel optimisation problem, where the inner level solves the Unit Commitment (UC) problem and outputs the optimal schedule given the prices of fuel, emissions, electricity, and the unknown plant parameters. The outer level then optimises over the plant parameters matching the historical production of each plant as closely as possible.

In this paper, first time ever artificial colony bee search is applied for thermal unit commitment. The main aim of doing thermal unit commitment scheduling is to optimize running while ensuring security and reliability. In recent era, lot of attention is given to the environmental conditions. All developed and few developing countries are worrying about emission of carbon dioxide from thermal generating stations. In first section of this paper unit commitment and Artificial Bee Colony (ABC) is discussed. In next section methodology for adopting ABC and general constraints of unit commitment are discussed in great detail. In last section application of ABC for unit commitment is implementation of proposed algorithm is shown and obtained results are presented. It is observed that ABC produces good results for unit commitment problem and easy to implement.

Evaluating the impact of sub-hourly unit commitment method on spinning reserve in presence of intermittent generators

Enhanced priority list unit commitment method for power systems with a high share of renewables