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Energy, resources and environment

(R.P. Hämäläinen, H. Ehtamo, P. Murto, E. Näsäkkälä)

Restructuring of electricity markets

There is a worldwide trend towards more competition in the organization of electricity supply. The Nordic countries have taken a leading position in this change that transforms the industries from centrally planned monopoly structures to deregulated markets. In Norway, the electricity supply was opened to competition already in the beginning of the 1990's, and Sweden and Finland followed a few years later. The Nordic countries have nowadays a common power exchange called Nordpool, and the markets have in practice merged into a single market. Also in the other EU countries the liberalization is on the way.

Motivated by these changes, we organized in March 1999 a workshop Coordination in deregulated electricity markets in Saariselkä, Finland. In P[PIN00], different aspects of the restructuring of the Finnish electricity market are discussed.

Investments in electricity markets

The restructuring of the electricity markets has important implications on the investment decisions of the market participants. Investments in production capacity are made by profit maximizing firms who face many important uncertainties. The rate of investment and the choice of production technologies have important implications, for example on the price of electricity, security of supply, and the emissions of CO2. The modeling of such issues is considered in T[MUR00]. In P[PIN03], a stochastic oligopoly model to describe the production and investment in a deregulated electricity market has been developed and applied to the Finnish case. The equilibrium, where each firm is assumed to maximize its profits, is solved numerically.

The main focus of the research has been on the effect of uncertainty on investments. Input and output prices, for example, are natural to model as stochastic variables. In that context, the investment opportunities are real options. In M[MURd] we consider energy investment, when a choice has to be made between fossil fuel and biomass fired production technologies. It is shown that when the choice of technology is irreversible, it may be optimal to postpone the investment even if it would otherwise be optimal to invest in one or both of the plant types. In P[MUR07] we consider wind power production and study the effects of technological and revenue related uncertainties on the timing of investment. In R[NAS05b] we study investments in gas fired power plants when also the natural gas prices are stochastic and the production process contains flexibility. Since there are some large producers in the market, the situation is also analyzed from the game theoretic perspective. In P[MUR02] we combine game theory with the real-option analysis of investment in different settings. T[MUR03] summarizes this research.

Production Planning and hedging in electricity markets

In a deregulated electricity market there is a publicly quoted market price set by the supply and demand of electricity. The emergence of the electricity markets has given birth to electricity derivative markets. An electricity derivative is a financial contract whose value depends on electricity price. A company whose earnings depend on the electricity price can use the electricity derivative markets to stabilize electricity dependent cash flows. In P[NAS05a] we study the partial hedging of stochastic electricity load pattern with static forward strategies. In P[NAS08] we illustrate how electricity forwards can be used in hydropower production planning and hedging.

Previous research:

Currently we do not have active research going on in these areas.

Dynamic pricing of electricity

( R.P. Hämäläinen, J. Ruusunen, M. Räsänen)

In today's electricity markets time-of-use (TOU) rates are used to reflect variations in the marginal production cost and to disencourage the consumption during peak load periods. TOU tariffs are static in nature: they are fixed, e.g., a year in advance and thus they cannot reflect the true instantaneous changes in the daily or hourly production conditions. Recent developments in the metering and communication technology make it possible to use innovative tariffs which are based on monitoring the customers and controlling the tariff in real time. Thus the market game becomes a dynamic one. The largest Finnish electricity producer Imatran Voima Ltd and four local electric utilities carried out dynamic pricing experiments from 1988 to 1993. We have constructes models to determine the effects of a dynamic tariff on a customer's daily usage pattern P[RÄS95a], P[RÄS95b], P[RÄS96].

Currently we are studying optimal control of indoor temperature in houses that have electricity space heating and where the TOU tariff can be changed dynamically P[HÄM02]. The problem is a dynamic multiobjective optimization problem where the criteria include maintaining comfortable indoor temperatures, minimizing energy use and minimizing the electricity bill. The dynamics of the buildings are identifield from experiments that are run in each house. Interactive communication devices and intelligent measurement systems for electricity consumption are installed into the houses. Electricity rates and consumer specific information are communicated to the customers using a system based on cellular paging technology. The goal of the project is to develop tools that can be used in building automation systems in "intelligent buildings". A decision support system, called MOHO, has been developed to help the consumers to optimize their electricity use. The project has been carried out in cooperation with Helsinki Energy, which is the city's utility company

[Figure 3.1.1]
Figure: Screen display of the MOHO program. Available here.

Agent based simulation of artificial electricity markets

(R.P. Hämäläinen, J. Mäntysaari, J. Ruusunen)

We have developed new approaches to the modelling of the customer reaction. The basic to problem here is to model how customers respond to different price patterns, also those that have not been used in the experiments, and then to find the price pattern that produces the desired changes in the utility's load pattern. Our idea is to model the consumers and producers as optimizing goal seeking agents and then simulate the electricity distribution system as an artificial market. This approach has connections to current research distributed artificial intelligence. We have developed a prototype software called Power Agents P[HÄM95d], P[HÄM96c].

[Figure 3.2.1]
Figure: A power agents program window showing the consumer agent's optimal reaction to a given dynamic tariff.

Thus far, the consumer agents represent residential customers. The customer response is assumed to be based on the maximization of the subjective valuation of services provided by the electricity used minus the price paid for it. Currently we are working with an agent model that describes the behavior of customers with electric space heating T[PAR94], T[HÄM96]. P[HÄM00b]. Different ways to use multicriteria optimization procedures are evaluated and the whole coordination problem is analyzed as a Stackelberg game P[HÄM97b].

We are extending our research on artificial markets the analysis of electricity production markets. There are new electricity exchange market places in the Nordic countries.

[Figure 3.2.2]
Figure: Players in the Scandinavian electricity market with sales (TWh) 1994.

Short-term load forecasting and load models

(J. Ruusunen, M. Räsänen)

Short-term load forecasting forms an important part of an energy management system. Accurate forecasts are a prerequisite for efficient production and purchase planning as well as for security assessment of the power system. We have developed a system for short-term load forecasting in communal utilities R[RUU88], T[KAR90]. The computer implementation of the system, called FLOATER, is now owned by IVO International, a large Finnish energy consulting company, and it is in operational use in three Finnish utilities.

Related to the work on dynamic pricing of electricity, we have developed approaches for modeling the load of different types of customers. Because of large measurement errors, robustness of the model is emphasized. Moreover, because of the very large number of data, computational considerations are extremely important. We have chosen the type of model where the load is modelled as the sum of the dominating rhythm component (daily or weekly rhythm) and the weather sensitive part of the load P[RÄS95a]. An example of the decomposition of the load into the rhythm component and the weather sensitive component is shown in the figure below. A computer implementation of the system, called LoadLab, has also been developed P[RÄS96]. The program has been used in the analysis of dynamic pricing experiments and in load analysis projects.

[Figure 3.3.1]
Figure: The components of the electrical load. From top to bottom: total load, the rhythm component, and the residual component together with outdoor temperature (dashed line). The measurements have been made in a transformer in the electricity network.

[Figure 3.3.2]
Figure: Screen display of the LoadLab program.

Electricity exchange

(H. Ehtamo, R.P. Hämäläinen, J. Ruusunen)

The electric utility industry has organized power pools to coordinate the generation and transmission of electric power. When there is an opportunity for cost savings, the members are confronted with the problem of allocating the savings in an equitable manner. We have developed contracts for multi--period electricy exchange considering situations where side payments between the companies are ruled out (P[EHT87, EHT88a, EHT89c, RUU89b, RUU91a, RUU92, RUU94a]). The exchange is accomplished through barter where the commodities are deliveries of electricity in the different periods of the cycle.

The axiomatic approach to bargaining can be used to define the equity properties of the cost allocation mechanism. Because the conditions for the compromise solution are, in general, nonseparable with respect to time the problem becomes computationally complex. We have developed a hierarchical computational method to solve these problems. The approach has turned out to be efficient in applications to real power systems.

Natural resource problems

(H. Ehtamo, R.P. Hämäläinen, V. Kaitala)

The use of control and game theory methods in international resource management problems is of growing importance. We have applied dynamic game theory to modelling and analysis of problems where two or more agents utilize the same or interdependent resources. Thus, the agents face dynamic decision making problems related to the choice between cooperative and noncooperative or myopic and foresighted management policies. We have analysed finite and infinite time horizon fishery games where agreements among the agents are not binding. Noncooperative solutions of games (P[KAI85a, KAI85b, DOC89, HÄM89a, KAI89a]) have been analysed. The properties of cooperative and noncooperative games have been combined in order to model negotiations where the purpose is to reach safe equilibrium agreement (P[HÄM84a, HÄM85a, KAI86, KAI87a, KAI88b, EHT88b, EHT93a, EHT95a]). The United Nations' Conference on Straddling Stocks and Highly Migratory Fish Stocks has been analysed using game theoretic approaches P[KAI95c], P[KAI95e], P[KAI98a], P[BJO00], R[LIN98].

The theory of Stackelberg (leader-follower, principal-agent) games has been aplied to model dynamic incentive contract. Models of output share incentives have been analyzed in connection with harvest share contracts in fishing under cartels and social coordination (P[HÄM86a, HÄM90c]). The Norwegian spring-spawning herring fishery has been studied using risk analysis P[TOU00], R[TOU98].

Pollution and environmental problems

(V. Kaitala, A. Kettunen)

Air pollution is mainly due to sulphur and nitrogen emisions which are partially deposited on the ground of the emitter countries. Due to the remote transportation of pollutants by winds, large amounts of the emissions fall on the ground in the neighbouring countries. Sulphur and nitrogen compounds affect the properties of soil, surface water, and ground water, for which reason this particular pollution process is referred to as acidification.

We have developed and analysed two- and three-country transboundary air pollution models in a game theory setting. The project analyses noncooperative and cooperative solutions of the acid rain game and evaluates the bilateral and trilateral emission reduction agreements among Finland, Russia, and Estonia P[KAI91e, KAI92c, KAI92d, TAH93, P[KAI95a]. P[KAI98b],

The climate on the earth is determined by the balance between energy received from the sun and energy reradiated back into space. The radioactively important gases, greenhouse gases (e.g. CO2, nitrous oxide, methane), are transparent to incoming visible radiation but they absorb invisible thermal radiation. The thermal balance on the earth is changed resulting in overall climate change. This phenomenon is referred to as the greenhouse effect.

Game theory analyses show that the design of cooperative programs for international environmental agreements may be a challenging and complicated task when we are dealing with stock pollutants and when the players are asymmetric with respect to emission volumes, emission abatement costs, or damages caused by the environmental change. The project analyses the dynamic properties of multilateral environmental agreements. In particular, we study the problem of constructing self-enforcing environmental equilibria in the context of environmental policy problems arising at an international level, in particular in the context of global climate change P[KAI95d].

Methane is an important greenhouse gas and contributes to the anticipated climate change. During the last two centuries, the atmospheric methane concentrations have been increasing significantly. In the biochemical cycle of methane, boreal peatlands act as methane sources. We have initiated a research project for analysing the methane emission dynamics in boreal peatlands P[KET96b].

[Figure 3.6.1]
Figure: The total emissions and depositions in Finland and in the four regions of the USSR in 1987. The emissions are divided into three parts - own contribution, to the neighbor, and to other areas - illustrating the fraction of the emission that will be deposited in the own area, in the neighbor country and other areas, respectively. The respective three parts in the deposition illustrate the origin (own emissions, transportation from the neighboring country, and that from other areas) of the depositing sulphur P[KAI91e].