# Generation Expansion Planning And Modeling Environmental Sciences Essay

Generation expansion planning is the process of determining WHICH, WHERE and WHEN new generation units should be available to satisfy the expected energy demand over a long range planning horizon.

GEP is considered to be difficult to solve for the uncertainty in input data such as forecast demand for electricity, economic and technical characteristic for new generating technologies, construction lead times, and government regulations [1].

Generally, GEP is a non-linear integer programming problem which can be solved by linear programming, non linear programming, dynamic programming and integer programming with some simplifications.

These mathematical models have been reviewed in [2] and [3]

Electric sector CO2 mitigation cost depends on how all the units in the electric grid interact to meet the demand. Therefore the GEP is a suitable method to assess the CO2 mitigation cost of the electric sector.

Assessment of CO2 mitigation can be broadly categories as being a “top-down” or “bottom-up” modeling strategy.

The “top-down” approach employs a macroeconomic framework and seeks to balance production and consumption across all sector of the economy. The focus is on financial flows across the whole economy and interaction between energy policies and the macroeconomic performance of the economy [4]. However, the model does not capture in detail the performance, cost, and dispatch of the generation unit in the electricity grid.

The “bottom-up” approach on the other hand looks at energy consumption in detail and examines the technology options available [4]. The model analyses the information on technologies and efficiencies to consider the direct cost and benefits enabling the overall cost of CO2 emissions to be calculated.

Both approaches are based on mathematical optimization techniques to identify the least-cost policy and technology alternatives [5] and can provide assessments spanning several decades or centuries.

Comparison on the characteristics between the “top-down” and “bottom-up” modeling approach as discuss by [6]:

The modeling results will likely depends on the basic assumption about technology cost and performance, rate of the technology deployment and change, policies and the baseline scenario [7].

An analysis into the mitigation potential of a technology such as CCS needs to cover engineering-economic perspective. A generation expansion planning for the electric utility can fulfill this requirement.

An optimization model will help to minimize the cost of CO2 abatement for a specific sector and selecting mitigating technologies options which offers the most economically competitive means in a specific scenario.

For an electricity generation pool, investment and retirement decision must be made and these interact with how installed capacity is used over time in a multi period analysis. The least cost generation expansion planning depends not only on the cheapest cost technology but other factors as fell which are in place as a constrain to the system. For example dispatch reordering might provide a lower-cost

Floudas stated that optimization models consist of [8]:

Variables: the variables can be continuous, integer or a mixed set of continuous and integer

Parameters: the parameters are fixed to one or more specific values, and each fixation defines a different model

Constraints: the limitation on the variables value

Mathematical relationships or equations: the mathematical models can be classified as equalities, inequalities and logical conditions. The model equalities are usually composed of mass balances, energy balances, equilibrium relations and engineering relations which describe the physical phenomena of the system. The model inequalities often consists of allowable operating limits, specification on qualities, performance requirement and bounds on availability’s and demand. The logical conditions provide the connection between the continuous and integer variables.

Mathematical models for optimization can be categorized as:

Linear programming (LP) problems

Mixed Integer Programming (MIP) problems

Dynamic Programming (DP) problems

Nonlinear Programming (NLP) problems

Linear programming

Linear programming is a method for finding the arrangement of activities that will maximizes or minimizes a defined criterion, subject to the operative constraints [9]. All relationships are expressed in fully linearized terms as the technique can deal only with situations where activities can be expressed in the form of linear equalities or inequalities, and where the criterion is also linear. Linear programming is a relatively simple technique which gives quick results and demands little mathematical complexity. Disadvantages are that all coefficients must be constant and that LP results in choosing the cheapest resource up to its limits before any other alternative is used at the same time for the same item. Also, LP models can be very sensitive to input parameter variations. This technique is used for almost all optimization models, and applied in national energy planning as well as technology related long-term energy research.

Mixed Integer Programming (MIP)

Mixed Integer programming (MIP) is actually an extension of Linear Programming which allow for greater detail in formulating technical properties and relations in modeling. Decisions such as yes/no (1/0) are admitted as well as nonconvex relations to discrete decision problems. MIP can be used when addressing questions such as whether or not to include a particular energy plant into a system.

Dynamic Programming

Dynamic programming is a method used to find optimal growth path. The solution of the original problem is obtained by splitting the original problem into sub problems for which optimal solutions are calculated. The original problem then can be solved using the calculated solutions of the sub problems. Dynamic programming overcomes many of the restrictions in linear programming, as follows [10]:

The capacity constraints on individual technologies actually make it easier, in contrast to most optimisation procedures where constraints increase computation times.

Load duration curves can be of any form, especially without the requirement of linear approximation.

There are fewer mathematical restrictions in dynamic programming, unlike the linearity and convexity requirements of linear programming.

This approach can also incorporate uncertainties in demand and in fixed and variable costs.

There are a number of energy analysis tools that are available for the analysis of the integration of carbon mitigation technologies into the energy system. A review focusing on the various computer tools for analyzing the integration of renewable energy into various energy systems can be found in [11].

Dynamic energy optimization models are technology oriented models that minimizes the total cost of the energy system.

WASP

The WASP (Wien Automatic System Planning Package) tool permits the user to find an optimal expansion plan for a power generating system over a long period, within the constraints defined by the planner. It is maintained by the IAEA (International Atomic Energy Agency), who have developed four versions of the program and distributed it to several hundred users. WASP is freely available to IAEA member states and requires 4–6 weeks of training. In WASP the optimum expansion plan is defined in terms of minimum discounted total costs. The entire simulation is carried out using 12 load duration curves to represent each year, for up to a maximum duration of 30 years. Conventional fossil-fuel, nuclear, and biomass power-plants can be simulated along with wind, wave, tidal, hydro power, and pumped-hydroelectric energy storage. Using the electricity demand for the future year, WASP explores all possible sequences of capacity additions that could be added to the system within the required constraints. These constraints can be based on achieving a certain level of system reliability, availability of certain fuels, build-up of various technologies, or environmental emissions. The different alternatives are then compared with one another using a cost function which is composed of capital investment costs, fuel costs, operation and maintenance costs, fuel inventory costs, salvage value of investments, and cost of energy demand not served. WASP has previously been used to evaluate the impact of CO2 taxation and introduction of biomass power generation in Thailand [11], to examine the future role of nuclear power in Korea [12], and to evaluate Thailand‘s dependence on natural gas and imported fuels [13].

1. Kagiannas, A.G., D.T. Askounis, and J. Psarras, Power generation planning: a survey from monopoly to competition. International Journal of Electrical Power & Energy Systems, 2004. 26(6): p. 413-421.

2. Hobbs, B.F., Optimization methods for electric utility resource planning. European Journal of Operational Research, 1995. 83(1): p. 1-20.

3. Zhu, J. and M. Chow, A review of emerging techniques on generation expansion planning. IEEE transactions on power systems, 1997. 12(4): p. 1722-1728.

4. Jaccard, M., A. Bailie, and J. Nyboer, CO2 emission reduction costs in the residential sector: Behavioral parameters in a bottom-up simulation model. Energy Journal, 1996. 17(4): p. 107-134.

5. Hourcade, J.C., R. Richels, and J. Robinson, Estimating the Cost of Mitigating Greenhouse Gases Economic and Social Dimensions of Climate Change. IPCC Second Assessment Report, 1996. Working Group 3: p. 263-296.

6. van Beeck, N., Classification of energy models, in Tilburg University and Eindhoven University of Technology. 1999.

7. Edmonds, J., J.M. Roop, and M.J. Scott, Technology and the economics of climate change policy, in Pew Center on Global Climate Change Report. 2000.

8. Floudas, C.A. and G.E. Paules, A mixed-integer nonlinear programming formulation for the synthesis of heat-integrated distillation sequences. Computers & Chemical Engineering, 1988. 12(6): p. 531-546.

9. Slesser, M., Macmillan Dictionary of Energy

1982, London: Macmillan Press.

10. Borison, A.B., et al., Evaluating R and D options under uncertainty. Volume 3. An electric-utility generation-expansion planning model. Final report. 1981. p. Medium: X; Size: Pages: 114.

11. Connolly, D., et al., A review of computer tools for analysing the integration of renewable energy into various energy systems. Applied Energy. 87(4): p. 1059-1082.

12. Lee, Y.E. and Y.B. Jung, Challenges of nuclear power for sustainable role in Korean energy policy. Energy Conversion and Management, 2008. 49(7): p. 1951-1959.

13. Nakawiro, T., S.C. Bhattacharyya, and B. Limmeechokchai, Electricity capacity expansion in Thailand: An analysis of gas dependence and fuel import reliance. Energy, 2008. 33(5): p. 712-723.

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