Joint planning of combined heat and power and electric power systems: An efficient model formulation

Gardner, Douglas T.; Rogers, J. Scott

doi:10.1016/s0377-2217(96)00221-4

Cited by 13 publications

(8 citation statements)

References 11 publications

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“…For example, in the framework of the deregulated electricity market, CHP electricity and heat production is not only determined by the electricity and heat demand, but is further related with the change of the electricity prices [38]. The definition of the appropriate constrains towards the most cost-efficient operation of a CHP plant is a complicated optimization problem and it has been thoroughly examined by the use of different model optimization techniques and under different CHP planning scenarios [37][38][39][40][41][42]. In this case, it is assumed that CHP total efficiency and 'power to heat' ratio values are fixed according to literature [3,43] and the CHP electricity and thermal conversion factors (or efficiencies) are also fixed according to Eqs.…”

Section: Description Of the Methodologymentioning

confidence: 99%

Industrial combined heat and power (CHP) planning: Development of a methodology and application in Greece

2011

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Section: Description Of the Methodologymentioning

confidence: 99%

Industrial combined heat and power (CHP) planning: Development of a methodology and application in Greece

2011

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“…The solution to the problem was obtained using a Fortran optimization routine. Gardner and Rogers [11] provided theoretical guidelines to include heat dispatch in electric dispatch problems. The resulting model for the joint planning of heat and power was a MILP model and was solved using GAMS/OSL.…”

Section: Literature Reviewmentioning

confidence: 99%

Operational planning of combined heat and power plants through genetic algorithms for mixed 0–1 nonlinear programming

Gopalakrishnan

Kosanovic

2015

Computers & Operations Research

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a b s t r a c tThis paper is concerned with short-term (up to 24 h) operational planning in combined heat and power plants for district energy applications. In such applications, heat and power demands fluctuate on an hourly basis due to changing weather conditions, time-of-day factors and consumer requirements. Plant energy efficiency is highly dependent on ambient temperature and operating load since equipment efficiencies are nonlinear functions of these parameters. In operational planning strategies, nonlinear equipment characteristics are seldom taken into account, resulting in plants being operated at sub-par efficiencies. In order to operate plants at highest possible efficiencies, scheduling strategies which take into account nonlinear equipment characteristics need to be developed. For such strategies, a mixed 0-1 nonlinear programming formulation is proposed. The problem is nonconvex and hence global optimality conditions are unknown. Classical techniques like branch-and-bound may not produce integer feasible solutions, may cut off the global optima and have an exponential increase in CPU time for a linear increase in planning horizon size. As an alternative, a solution method through genetic algorithms is proposed in which genetic search is applied only on 0-1 variables and gradient search is applied on continuous variables. The proposed method is a nonlinear extension of the one originally developed by Sakawa et al. [Sakawa M, Kato K, Ushiro S. Operational planning of district heating and cooling plants through genetic algorithms for mixed 0-1 linear programming. Eur J Operat Res 2002;137(3):677-87].Numerical experiments show the proposed genetic algorithm method is more consistent in finding integer feasible solutions, finds solutions with lower optimality gaps and has reasonable CPU time as compared to branch-and-bound. From an application perspective, the proposed scheduling strategy results in 5-11% increase in plant energy efficiency.

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“…The first term on the left-hand side is the cumulative heat volumes from all components. Constraints (8)- (10) encode non-convex characteristics. Constraints (8) disallow operations in non-active subareas or between subareas by forcing the corresponding x j,t to zero.…”

Section: Parameters ϕ(J)mentioning

confidence: 99%

“…Traditional CHP production is usually applied in backpressure plants, where the joint characteristic can typically be represented by a convex (linear programming, LP) model [9][10][11][12][13][14]. But the convexity assumption may not be valid for more advanced production technologies such as backpressure plants with condensing and cooling options, gas turbines and combined gas and steam cycles.…”

Section: Introductionmentioning

confidence: 99%

A two phase approach for the bi-objective non-convex combined heat and power production planning problem

Rong

Figueira

Lahdelma

2015

European Journal of Operational Research

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In this paper, we deal with the bi-objective non-convex combined heat and power (CHP) planning problem. A medium and long term planning problem decomposes into thousands of single period (hourly) subproblems and dynamic constraints can usually be ignored in this context. The hourly subproblem can be formulated as a mixed integer linear programming (MILP) model. First, an efficient two phase approach for constructing the Pareto Frontier (PF) of the hourly subproblem is presented. Then a merging algorithm is developed to approximate the PF for the multi-period planning problem. Numerical results with real CHP plants demonstrate the effectiveness and efficiency of the solution approach using the Cplex based ε-constraint method as benchmark.

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Joint planning of combined heat and power and electric power systems: An efficient model formulation

Cited by 13 publications

References 11 publications

Industrial combined heat and power (CHP) planning: Development of a methodology and application in Greece

Industrial combined heat and power (CHP) planning: Development of a methodology and application in Greece

Operational planning of combined heat and power plants through genetic algorithms for mixed 0–1 nonlinear programming

A two phase approach for the bi-objective non-convex combined heat and power production planning problem

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