This paper presents a new mixed-integer linear formulation for the unit commitment problem of thermal units. The formulation proposed requires fewer binary variables and constraints than previously reported models, yielding a significant computational saving. Furthermore, the modeling framework provided by the new formulation allows including a precise description of time-dependent startup costs and intertemporal constraints such as ramping limits and minimum up and down times. A commercially available mixed-integer linear programming algorithm has been applied to efficiently solve the unit commitment problem for practical large-scale cases. Simulation results back these conclusions

A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem

A communication infrastructure is an essential part to the success of the emerging smart grid. A scalable and pervasive communication infrastructure is crucial in both construction and operation of a smart grid. In this paper, we present the background and motivation of communication infrastructures in smart grid systems. We also summarize major requirements that smart grid communications must meet. From the experience of several industrial trials on smart grid with communication infrastructures, we expect that the traditional carbon fuel based power plants can cooperate with emerging distributed renewable energy such as wind, solar, etc, to reduce the carbon fuel consumption and consequent green house gas such as carbon dioxide emission. The consumers can minimize their expense on energy by adjusting their intelligent home appliance operations to avoid the peak hours and utilize the renewable energy instead. We further explore the challenges for a communication infrastructure as the part of a complex smart grid system. Since a smart grid system might have over millions of consumers and devices, the demand of its reliability and security is extremely critical. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout. Security is a challenging issue since the on-going smart grid systems facing increasing vulnerabilities as more and more automation, remote monitoring/controlling and supervision entities are interconnected.

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1315&context=electricalengineeringfacpub

A Survey on Smart Grid CommunicationInfrastructures: Motivations, Requirements andChallenges

The development of energy management tools for next-generation PhotoVoltaic (PV) installations, including storage units, provides flexibility to distribution system operators. In this paper, the aggregation and implementation of these determinist energy management methods for business customers in a microgrid power system are presented. This paper proposes a determinist energy management system for a microgrid, including advanced PV generators with embedded storage units and a gas microturbine. The system is organized according to different functions and is implemented in two parts: a central energy management of the microgrid and a local power management at the customer side. The power planning is designed according to the prediction for PV power production and the load forecasting. The central and local management systems exchange data and order through a communication network. According to received grid power references, additional functions are also designed to manage locally the power flows between the various sources. Application to the case of a hybrid supercapacitor battery-based PV active generator is presented.

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Energy Management and Operational Planning of a Microgrid With a PV-Based Active Generator for Smart Grid Applications

With the fast-paced changing technologies in the power industry, new power references addressing new technologies are coming to the market. So there is an urgent need to keep track of international experiences and activities taking place in the field of modern unit-commitment (UC) problem. This paper gives a bibliographical survey, mathematical formulations, and general backgrounds of research and developments in the field of UC problem for past 35 years based on more than 150 published articles. The collected literature has been divided into many sections, so that new researchers do not face any difficulty in carrying out research in the area of next-generation UC problem under both the regulated and deregulated power industry.

Unit commitment-a bibliographical survey

Optimization Techniques with FORTRAN

The work was conducted with the aim of finding a general method for solving the unit commitment (UC) problem. The proposed algorithm employs the evolutionary programming (EP) technique in which populations of contending solutions are evolved through random changes, competition, and selection. In the subject algorithm an overall UC schedule is coded as a string of symbols and viewed as a candidate for reproduction. Initial populations of such candidates are randomly produced to form the basis of subsequent generations. The practical implementation of this procedure yielded satisfactory results when the EP-based algorithm was tested on a reported UC problem previously addressed by some existing techniques such as Lagrange relaxation (LR), dynamic programming (DP), and genetic algorithms (GAs). Numerical results for systems of up to 100 units are given and commented on.

An evolutionary programming solution to the unit commitment problem

Dynamic economic dispatch (DED) is one of the main functions of power generation operation and control. It determines the optimal settings of generator units with predicted load demand over a certain period of time. The objective is to operate an electric power system most economically while the system is operating within its security limits. This paper proposes a new hybrid methodology for solving DED. The proposed method is developed,in such a way that a simple evolutionary programming (EP) is applied as a based level search, which can give a good direction to the optimal global region, and a local search sequential quadratic programming (SQP) is used as a fine tuning to determine the optimal solution at the final. A ten-unit test system with nonsmooth fuel cost function is used to illustrate the effectiveness of the proposed method compared with those obtained from EP and SQP alone.

A hybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function

Dynamic economic dispatch (DED) is one of the main functions of power generation operation and control. It determines the optimal settings of generator units with predicted load demand over a certain period of time. The objective is to operate an electric power system most economically while the system is operating within its security limits. This paper proposes a new hybrid methodology for solving DED. The proposed method is developed in such a way that a simple evolutionary programming (EP) is applied as a based level search, which can give a good direction to the optimal global region, and a local search sequential quadratic programming (SQP) is used as a fine tuning to determine the optimal solution at the final. A ten-unit test system with nonsmooth fuel cost function is used to illustrate the effectiveness of the proposed method compared with those obtained from EP and SQP alone. Index Terms—Dynamic economic dispatch (DED), evolutionary programming (EP), global solution, power generation operation and control, sequential quadratic programming (SQP), stochastic optimization techniques. solutions for the optimization problem. This method is similar to the local search technique in optimization, which can only guarantee a local optimum solution. However, the SA method in addition employs a probabilistic approach in accepting can- didate solutions in its solution process such that it can jump out from the local optimum solutions. The SA technique has been applied in many power system problems. However, appropriate setting of the control parameters of the SA based algorithm is a difficult task and the speed of the algorithm is slow when ap- plied to a real power system. GA, invented by Holland (19) in the early 1970s, is a stochastic global search method that mimics the metaphor of natural biological evaluation. GA operates on a population of candidate solutions encoded to finite bit string called chromosome. In order to obtain optimality, each chromo- some exchanges information by using operators borrowed from natural genetic to produce the better solution. The GA uses only objective function information, not derivative or other auxiliary knowledge. Therefore, GA can deal with the nonsmooth, non- continuous and nondifferentiable functions, which actually exist in a practical optimization problem. On the other hand, EP is also a global search method starting from a population of can- didate solutions, and finds solution in parallel using evaluation process. Both GA and EP can provide a near global solution. However, the encoding and decoding schemes essential in the GA approach are not needed in DED problem. Therefore, EP is faster in speed than GA in this case. Although GA and EP seem to be good methods to solve opti- mization problem, sometimes the solutions obtained from both methods are just near global optimum ones. And also GA and EP take long computation times in order to obtain the solutions. Therefore, the combine method of GA/EP and another optimiza- tion techniques were applied in power system application such as optimal power flow problem (20), unit commitment problem (21), and economic dispatch in (22)-(25). However, all the pre- vious works mentioned above neglected the nonsmooth char- acteristic of generator, which actually exist in the real power system. In this paper, the application of EP combined with SQP for solving DED is proposed. The hybrid proposed method uses the property of EP, which can provide a good solution even the problem has many local optimum solutions at the beginning. Then, the local searching property of SQP is used to obtain a final solution. Basically, the hybrid method can be divided into two parts. The first part employs EP to obtain a near global so- lution, while the second part employs SQP to determine the op- timal solution. In order to show the effectiveness of the proposed method over EP and SQP alone, ten units test system with non- smooth fuel cost function is used in this paper. The results of the

A Hybrid EP and SQP for Dynamic Economic

In this paper, the authors propose a fuzzy-optimization approach to dynamic economic dispatch, considering the uncertainties in deregulated energy and reserve markets. The methodology was developed from the viewpoint of a generation company wishing to maximize its own profit and to hedge its risks as a participant in the energy market and 10-min spinning reserve market. The uncertainties in the current paper were represented with fuzzy numbers, and consist of the demand and reserves required in each market, prices cleared in each market, and the probability that reserves are called upon in actual operation. The energy and reserve markets were coordinated by the generator ramp rate limits. The optimal amounts of power and reserve were determined by solving the optimization problem. Two reserve payment methods, payment for power delivered and payment for reserve allocated, were investigated in this paper.

A fuzzy-optimization approach to dynamic economic dispatch considering uncertainties

With the opening of the power industry to competition, the power system structure is changing. According to these changes, power system operation, planning, and control need modifications. In the past, utilities had to produce power to satisfy their customers with objective to minimize costs and all demand/reserve were met. However, it is not necessary in a restructured system. Under new structure, generation companies (GENCOs) schedule their generators with objective to maximize their own profit without regard for system social benefit. Power and reserve prices become important factors in decision process. This paper proposes a new tool, profit-based unit commitment (UC) considering power and reserve generation. The proposed method helps GENCO to make a decision, how much power and reserve should be sold in markets, and how to schedule generators in order to receive the maximum profit. A hybrid method between Lagrange relaxation (LR) and evolutionary programming (EP) is used for solving this problem. Simulations are carried out to show the performance of the proposed methodology.

Hiroyuki Kita

Papers

An evolutionary programming solution to the unit commitment problem

A hybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function

A Hybrid EP and SQP for Dynamic Economic

A fuzzy-optimization approach to dynamic economic dispatch considering uncertainties

A hybrid LR-EP for solving new profit-based UC problem under competitive environment