by Antonis Papaemmanouil - Research assistant, Power systems laboratory ETH Zurich

In this article, the interaction of three main exercises of power systems expansion planning - the production mix development, the decisions on environmental policy and the investments in transmission capacity - is going to be analyzed.
A common strategy for power production capacity expansion takes into account the load evolution and defines the least cost solution, usually without taking into account the capability of the transmission network. Under perfect market conditions, the cheapest power has to be transmitted to the delivery point, increasing power exchanges and making the transmission network weaker and often congested. Therefore, the transmission network itself and the effects of new installed generation capacity on it, cannot be neglected from the planning process.
The system operation target has been changed recently, from minimizing only the production cost to the maximization of social welfare (SW) as defined from traditional energy economics, e.g. the difference of consumer benefit (CB) and production cost (PC). Figure 1 describes the social welfare for a linear and a step-wise supply curve, when the demand responds to price changes with specific price elasticity. Any change in demand or supply affects the equilibrium point and thus the social welfare. Changes in supply and demand curve can very often be occurred, mostly when insufficient transmission capacity is available. In case that power exchanges between nodes are limited by inadequate transmission capacity at the cross borders or in one control area of the system, more expensive power plants have to be dispatched and thus the solution is not economic optimal but suboptimal in order to be satisfied the physical constraints of the system. As electricity market auctions correspond to physical transactions, the transmission grid availability plays a very important role in the decision of power plants dispatch, in market price formation and consequently in the decision of new investments.

Except for inadequate transmission capacity the equilibrium point is affected also from new investments in generation. Installation of base or peak load power plants moves the supply curve to the right or to the left respectively. Investments on renewable energy sources in the long-term planning are ranked as low or middle cost power plants according to their form. Controllable renewable power plants like pump-storage hydro are in the middle price range, whereas stochastic power in-feed from wind or solar parks in the low price range neglecting the investment and O&M costs.

Another parameter that influences the market equilibrium price is the applied governmental energy policy. The most usual economic tools are either the environmental taxes, which lead to higher production prices for conventional power plants or the technology subsidies, which lead to lower production prices for renewables. The first moves the supply curve more up and the second moves the supply curve more down, which corresponds to a certain decrease or increase of social welfare respectively. Yet, the European emission trading system (EU ETS) for green certificates influences the supply curve as well, as it introduces additional costs to the production of conventional power plants, however generation companies may manage to reduce their CO2 emissions cost by expanding their generators portfolio in several countries. Many European studies (ExternE, CAFE, NEEDS, CASES) have tried to evaluate the external costs imposed by the power production to the society.
As long as these are not included in the electricity prices, market failures most often arise. In order to define expansion projects, right price signals are needed. In case that these external costs are internalized in the production costs, much higher market prices for conventional technologies will appear, giving more initiatives for investing in new environmental friendly technologies. The internalization causes a deficit in social welfare due to price increment, which deficit is usually smaller than the total environmental benefits from the decrease of the fossil fuel power plants production. Summarizing, market prices depend a lot on available production capacities, thus on strategic behavior of generators, on the generation mix, the load, the weather conditions, the applied environmental policy as well as on unexpected network occurrences and congestions. Therefore, targeting to maximize the social welfare in power systems how can we be sure that this is the real maximum? And if not how is it possible to talk about optimal transmission expansion or reinforcement when so many uncertainties influence the market prices and so the price signals that facilitate the decisions on transmission investments?


This uncertain environment calls for new ways of planning both generation mix development and power transmission network considering also emissions reduction targets and market structures in order to create sustainable future electricity networks as described in Figure 2. The expansion planning is a multidimensional problem and the identification of optimal projects depends a lot on the decision maker preferences. The project Towards future electricity networks, that is ongoing at Power Systems Laboratory of ETH Zurich, deals with the modern challenges of power systems reconstruction aiming to provide a new planning tool to the academic society and the power systems planners.


The tool consists of two parts. The first part includes cost-benefit analysis (CBA) giving economic information on the profitability of an investment plan and the second part is based on a multi-objective optimization algorithm that produces trade-off curves providing useful information to the planner, who can decide whether the most profitable investment corresponds to his/her preferences. The tool assumes a coordinated expansion planning where the schedule of future generation capacity and load is given to the planner and several parameters for the environment, the economy and the security of supply are considered, see Figure 3. Assuming a conceptual network with a basic generation mix, e.g. coal, nuclear, wind, gas and solar power, a small example of trade-off curves is presented below. The analysis is based on optimal dispatching of the power plants in order to maximize the aforementioned social welfare.

In Figure 4 the linear dependency of internalized environmental costs, transmission network availability and social welfare is presented. The internalized costs refer to the level of environmental tax that is implied on “conventional” generated power. As previously stated, for constant demand and unchanged generation mix, taxes lead to lower social welfare as the generated power becomes more expensive. In contrast, higher availability of the transmission network leads to lower generation prices and thus to higher social welfare, since congestions are avoided and cheaper power can be dispatched. For every point on the rectangular A-B-C-D corresponds a projected point on the line A-C, assigned to social welfare (SW).

The same principles apply to the investigated system as presented in Figure 5. The initial point of the analysis is (X, Y) = (0, 0.8) and all other combinations are compared with that. The target is to provide the decision maker with more analytical information based on multi-objective optimization. It’s obvious that the social welfare is maximized, when additional transmission capacity is available, without any change in the supply mix and demand. However when additional costs are internalized in the marginal costs of power production, the social welfare decreases and reaches its minimum for the minimum availability of the transmission network. Hence, additional transmission capacity results to lower market prices, and accordingly to higher social welfare. When additional taxes are imposed to the polluters, the linear behavior remains although lower values appear. In the analysis, the indicator of avoided environmental costs (AEC) is introduced. It represents the difference in environmental costs that arise for every operational condition compared to the initial case.

Figure 5 depicts that the optimal point reflects to 100% internalized environmental costs and 80% usage of the transmission lines capacity. For higher utilization of the transmission network more power can be delivered to the nodes, which in our case comes from conventional power plants as the installed capacity of gas and coal is larger than nuclear and renewables. Thus, the minimum point of the figure appears for maximum utilization of the transmission network and zero taxation. Higher taxation level is required in order to compensate the negative impact of lower transmission constraints, however even though for maximum internalization level the maximum AEC cannot be obtained. In this case either a stronger environmental policy or more investments in green generation technologies is needed.
Regarding the market price, it increases for very high internalization level and decreases when more transmission capacity is available, which means that power plants with lower marginal costs can be dispatched, Figure 5. Thus, the optimal AEC point corresponds to the highest system price point. The correlation between Figure 5 and 6 is obvious.

In case of increased wind power production the behavior of the system concerning social welfare is the same, however different values are assigned to the system status. The system behaves different for AEC and system price and so according to the preferences of the decision maker another optimal solution can be found. The latter happens also in case of other generation expansion plans.

The major difference has been noticed in AEC, see Figure 8, where the optimum has changed and is no more at the edge of the curve. It still corresponds to the highest environmental costs internalization but the installation of new green power allows 10% higher utilization of the transmission lines, keeping the AEC to its maximum. Additionally, much higher AEC is obtained compared with the base case scenario.



Concluding, market prices and therefore the price signals for new investments in transmission and generation are influenced by many parameters. The energy policy, the generation mix development, the transmission network constraints and the market structure interact with each other. Thus, new methods and tools are needed and an analytical example has been presented. Of course, for different systems different diagrams for AEC and system prices occur. Only the implementation of an environmental policy imposing additional taxes, or only additional transmission or generation capacity is not enough when a sustainable future electricity network is the target. The decision maker has to combine all these elements and find the optimal solution according to his/her preferences.