1. Universidad Tecnológica de Pereira
A Multi-Objective Analysis for Planning Electric and Natural Gas Distribution Networks
Carlos A. Saldarriaga, Student Member, IEEE, Ricardo A. Hincapié, Student Member, IEEE, Harold Salazar, Senior Member, IEEE
Universidad Tecnológica de Pereira
Montevideo, ISGT-LA
Oct, 2015

2. Outline Problem description Mathematical formulation
Solution methodology
Numerical results
Concluding remarks

3. Problem Description
The most recent findings of natural gas and the low cost of extraction, transportation, and marketing in some developing economies have opened the possibility of incorporating distributed generation (DG) based on natural gas into electricity distribution networks.

4. Problem Description
The oil price volatility and the foreign exchange rate volatility affect foreign investment in developing economies that have high dependence of commodity exports. One special case is the investment in energy sector that is highly sensitive to such variations. In this context, traditional expansion planning methodologies applied to energy sector have to be carefully examined because:
1) foreign investors or private debt funds execute most of the expansion plans and,
2) financial benefits might not be realized due to financial uncertainty.

5. Problem Description
This work proposes a multi-objetive planning model that integrates electricity distribution and natural gas networks—that is, it considers the two networks as one system.
Integrated planning might achieve lower investment and operational costs for both systems
Overcomes the many problems that a natural gas network might face if a new DG installed in the electricity network requires excessive gas.
Integrated planning is also seen by many utilities (which operate in the electricity and natural gas sectors) as an alternative to offering low-cost service to their customers.

6. Problem Description
Taken from: C.A. Saldarriaga, R.A. Hincapié and H. Salazar, "A Holistic Approach for Planning Natural Gas and Electricity Distribution Networks," IEEE Trans. on Power Systems, 28(4), pp , Nov

7. Problem Description
A multi-objective optimization model is proposed in this paper to find a set of optimal solutions (Pareto solutions). The optimization model is a mixed integer nonlinear model. Two objective functions are considered in this paper: the total investment cost and operative cost of electricity network (technical losses cost).

8. Mathematical Formulation
Objective Function
OF1 =
OF2 =

9. Mathematical Formulation
Objective Function
Demand
… l … nL
Level

10. Mathematical Formulation
Optimization model constraints
The optimization model has different constraints, which are grouped into three categories. The first and second categories are physical and operational constraints of the electricity and the natural gas networks respectively. The last group is associated with the operational characteristics of the natural gas DG that link the electricity and the natural gas networks.
First category
Third category
Second category

11. Mathematical Formulation
Optimization model constraints
Nodal equations: electric power (Kirchhoff laws) and natural gas (Conservation mass law)
Maximum capacity of the electric system elements (feeders, substations and distributed generation) and natural gas system elements (pipelines, citygates)
Operative limits of the electric system (nodal voltages) and natural gas system (nodal pressures)
Natural gas distributed generation consumption

12. Solution Methodology
The optimization model is a mixed-integer nonlinear programming model.
This work uses a master/slave methodology as a solution strategy in which the master proposes a solution that is subsequently evaluated by the slave.
We used two types of master program, these are:
1) Strength Pareto Evolutionary Algorithm II (SPEA 2).
2) Chu-Beasley (CB) genetic algorithm.
and the slave program is calculated by solving an integrated optimal power flow that is derived from the mathematical formulation.

13. Numerical Results

14. Numerical Results

15. Numerical Results

16. Concluding remarks
In this paper the impact of operative costs and investment costs in the integrated planning of natural gas and electric distribution systems is presented. To describe this problem a multi-objective mathematical model is proposed, in order to verify the effect of multiple objectives.
Numerical results show that the two proposed objectives (investment cost and technical losses) are conflicting objectives since decreasing technical losses necessary implies more investment by means of the need of more natural-gas based DG (that also requires more investment on the natural gas network) and higher wire gauges (that are more expensive).

17. Concluding remarks
Numerical results also show that a multi-objective approach is more suitable since it is able to provide to the network planner different alternatives to be analyzed. That is, the Pareto optimal frontier for the test system has 150 different solutions in contrast to the single solution of a single objective optimization. In fact, the single solution provide by the Chu-Beasly algorithm is part of the Pareto frontier. This paper also provides a heuristic method to help the decision maker (or planner) to select one solution of the Pareto optimal frontier.
Numerical results also show that a multi-objective optimization approach does not require a prior knowledge of which objective should have a higher weight. In other words, a single objective optimization approach requires that the planner should have a clear understanding of which objective should have a higher weight so that the values of 1 and 2 can be fixed. These values are not relevant (in fact, they do not exist) for the multi-objective approach and it becomes one of its more important advantages.

18. Concluding remarks
Finally, a multi-objective approach for integrated electricity and natural gas expansion planning is more convenient since numerical results make clear that a single objective does not provide a wide variety of solutions. Decision makers often prefer to have multiples solutions since most of fast growing economies are facing a challenging environment that requires different alternatives before a final decision can be made.

19. THANK YOU