Traditionally, simulation of business process is used to support strategic decision-making. In this case, simulation is used as a tool to analyze long-term effects of certain decisions. Simulation is rarely used for management control and operation control, because building a simulation model takes too much time to evaluate short effects [9]. However the short-term operation and constraints are critical also on long-term strategic decision-making problems in the electric power system because the electricity market should be operated on the physical system. Therefore it is required to consider long-term and short-term problems as the components interacting with each other in a problem.
In this section it is exampled that long-term and short-term problems are related to renewable energy sources in electricity market. Economic aspects of renewable source investments are dealt with in the first long-term problem section, and operational issues of renewable generator like coordinated dispatch with other thermal plants in the second short-term problem section.
7.1. Long-term Investment Problem
Electricity market prices are published by (Korea Power Exchange KPX) on its webpage, and those historical data used for forecasting future market prices. These data could be retrieved from (Electric Power Statistics Information System EPSIS) periodically as input data for IDMS.
There are lots of methods and tools for forecasting the electricity market prices. The most simple and popular ones are statistics based function provided by spreadsheet programs, which mostly uses historical data. As more specialized tools, there are several computerized tools for electricity market simulation provided by commercial vendors. Figure 18 shows the electricity market prices recently published by KPX (Korea Power Exchange) for one month of March in 2009 [10]. The average price for the month is 145[Won/kWh].
One month is not a long-term in electricity market and it shows any increasing or decreasing trend in Figure 18. The average price could be used for future investment plan by reflecting the load growth and the inflation rate as a simple scenario that there is no change on fuel costs and fuel mix ratio. When there is capacity investment or fuel cost variation, the long term market prices could go down as well as up.
A scenario about the market price is assumed as shown in Figure 18 for the case study based on the concepts given in Figures 18 and 19. The product costs of wind power energy are variable dependent on the wind resource quantity and the load factors of the wind generators, so it is quite difficult to quantify the unit cost per unit energy (kWh). However it is required to do economic assessments.
Production costs are applied like in Figures 21 and 22 in this case study, which are similar with the current cost levels of renewable energy production in Korea. As the technology advances the unit cost of renewable energy production is expected to decrease year by year. Figure 23 shows the generation costs of conventional thermal generators.
Feed-in tariffs are temporal measures for supporting the introduction of renewable energy sources until they have the economic competitiveness compared to conventional fossil fueled generators. The purchasing prices are applied to wind and solar energy resources in feed-in tariffs at the level of Figures 24 and 25. The price for wind is discounted 2% every 3 years and solar for 4%.
Feed-in tariffs are uncertain variables determined by government policies because policy-related variables are very hard to forecast. Therefore it is required to build various scenarios on feed-in tariffs to minimize the risks by applying the wrong payoff price to the profitability estimation of renewable energy sources.
Emission costs are changing on real-time basis correlated with the price of emission credits according to the balancing condition between supply and demand in emission trading markets. The annually averaged prices are used for this case study for simplification. And the prices are multiplied by a multiplier (0.02) reflecting the transient status of emission costs applied to generation costs.
The emission quantity[g] from each energy source per unit electricity [kWh] production is illustrated in Figure 25.
Through the emission credit price [Won/ton] in Figure 26 and the emission quantity [g/kWh] in Figure 27, the emission cost can be recalculated as the unit [Won/kWh] in Figure 28.
Considering all the data till now decision-making system based on the algorithm in Figures 14 and 15 gives the profitability result of each generation source over time horizon. This result is based on the assumption given in the beginning, so the result could be different by another assumption. However it is expected to be similar to the trend in which nuclear and renewable energy sources have good profitability.
Nuclear, solar energy and wind energy show good profitability compared to other energy resources in Figure 29 and that trend will be stronger as the emission cost loads are heavier by increasing the multiplier to the value more than 0.02. Through the automation of this decision-making process, energy companies are expected to assess multiple alternatives on renewable investment plans.
7.2. Short-term Operation Problem
Renewable energy sources as wind power and photovoltaics are intermittent in production and therefore not always available in the power supply, when needed. This of course can imply that conventional power capacity is to be available to compensate for the missing production from renewable plants [11]. It is necessary to coordinate the renewable energy operation with other thermal plants both on economic aspects and system aspects because thermal plants have the most flexible ramp rate compared to other energy sources. This has also been studied in hydrothermal coordination for a long time. Traditionally, hydrothermal coordination is formulated as a cost minimization problem, that is, to minimize the total system cost (usually, the thermal production cost) [12]. Renewable energy sources have intermittent supply characteristics more difficult to control compared to hydroenergy sources. As technology evolves renewable energy sources have been being more controllable. But it is not yet enough, so they are also required to be supported by thermal plants.
It is assumed that load data and renewable outputs are forecasted as in Figure 30. Load data is forecasted by a module in (Energy Management System EMS) of KPX and published at the homepage.
Historical data acquired from SCADA system are used for load forecast. SCADA system retrieves data from the RTU installed in each substation. SCADA system and EMS are also connected with each other's data exchange based on the (InterControl Center Communication Protocol ICCP).
Considering the forecasted load and expected renewable outputs, EMS issues the dispatch order to thermal generators to meet the net load subtracted by the renewable output from the originally forecasted load. In the case of Figure 31 the net load is calculated as shown in Figure 33.
The net load is supplied by thermal generators considering the uncertainties and up/down ramp rates of thermal generators. There are two uncertain variables on this process. One is forecasting errors in load forecast, and the other is in renewable output. Therefore it is required for system operator or generation companies to be updated with real-time information and thereby follow the net load variation. The information is mainly collected from SCADA system, but it is recommended to acquire from many other metrological sensor devices for predicting renewable resources. Wireless sensor networks seem very appropriate for this purpose on the aspect of power supply. Energy conservation plays a crucial role in wireless sensor networks since such networks are designed to be placed in hostile and nonaccessible areas. While battery-driven sensors will run out of battery sooner or later, the use of renewable energy sources such as solar or gravitation may extend the lifetime of a sensor [13]. Interaction and coordination between generators could mitigate the uncertainties caused by intermittent property of renewable energy sources based on information acquired periodically from the existing SCADA system and wireless networks.
The data collected from various network routes are analyzed and used for IDMS for optimal decision-making on short-term operational problems.