There is a large thermal power plant. It works as usual: it burns gas, generates heat for heating houses and electricity for the general network. The first task is heating. The second is to sell all generated electricity on the wholesale market. Sometimes it snows even when it's cold and the sky is clear, but this is a side effect of the operation of cooling towers.
The average CHPP consists of a couple of dozens of turbines and boilers. If the required volumes of electricity and heat generation are precisely known, then the problem is reduced to minimizing fuel costs. In this case, the calculation is reduced to the choice of the composition and percentage of loading of turbines and boilers in order to achieve the highest possible efficiency of the equipment. The efficiency of turbines and boilers is highly dependent on the type of equipment, operating time without repair, operating mode, and much more. There is another problem, when, with known prices for electricity and volumes of heat, it is necessary to decide how much electricity to generate and sell in order to get the maximum profit from working on the wholesale market. Then the optimization factor - profit and equipment efficiency - is much less important. The result can be a regime where the equipment is completely inefficient, but the entire amount of electricity generated can be sold at a maximum margin.
In theory, all this has long been clear and sounds beautiful. The problem is how to do it in practice. We started simulation of the operation of each piece of equipment and the entire station as a whole. We came to the CHPP and began to collect the parameters of all nodes, measuring their real characteristics and evaluating the work in different modes. Based on them, we created accurate models to simulate the operation of each piece of equipment and used them for optimization calculations. Looking ahead, I will say that we have gained about 4% of real efficiency simply due to mathematics.
Happened. But before describing our decisions, I will talk about how the CHP works in terms of decision-making logic.
Basic things
The main elements of the power plant are boilers and turbines. The turbines are driven by high-pressure steam, which in turn drives electric generators, which generate electricity. The rest of the steam energy is used for heating and hot water. Boilers are places where steam is created. It takes a lot of time (hours) to heat up the boiler and accelerate the steam turbine, and these are direct fuel losses. The same goes for load changes. You need to plan things like this in advance.
CHP equipment has a technical minimum, which includes a minimum, but at the same time stable operating mode, in which it is possible to provide sufficient heat to homes and industrial consumers. Usually, the amount of heat required is directly dependent on the weather (air temperature).
Each unit has an efficiency curve and a point of maximum efficiency: with such and such a load, such and such a boiler and such and such a turbine provide the cheapest electricity. Cheap - in the sense of a minimum specific fuel consumption.
Most of our CHPPs in Russia are with parallel connections, when all boilers operate on one steam collector and all turbines are also fed from one collector. This adds flexibility when loading equipment, but greatly complicates the calculations. It also happens that the station equipment is divided into parts that work on different collectors with different steam pressures. And if you add expenses for internal needs - the operation of pumps, fans, cooling towers and, to be honest, saunas right behind the fence of the thermal power plant - then the devil will break his leg.
The characteristics of all equipment are non-linear. Each unit has a curve with zones where the efficiency is higher and lower. It depends on the load: at 70% efficiency there will be one, at 30% - another.
The equipment is different. There are new and old turbines and boilers, there are units of different designs. Choosing the right equipment and loading it optimally at the points of maximum efficiency, you can reduce fuel consumption, which leads to cost savings or higher margins.
How does the CHP plant know how much energy needs to be produced?
Planning is carried out three days ahead: the planned composition of the equipment becomes known in three days. These are the turbines and boilers that will be included. Relatively speaking, we know that five boilers and ten turbines will operate today. We cannot turn on other equipment or turn off the planned one, but we can change the load for each boiler from minimum to maximum, and increase and decrease power by turbines. The step from maximum to minimum is from 15 to 30 minutes, depending on the piece of equipment. Here the task is simple: choose the optimal modes and keep them, taking into account operational adjustments.
Where did this set of equipment come from? It was determined by the results of trading on the wholesale market. There is a power and electricity market. In the capacity market, manufacturers submit an application: βThere is such and such equipment, these are the minimum and maximum capacities, taking into account the planned withdrawal for repair. We can deliver 150 MW at this price, 200 MW at this price, and 300 MW at this price.β These are long term claims. On the other hand, large consumers also apply: βWe need so much energy.β Specific prices are determined at the intersection of what energy producers can give and what consumers are willing to take. These powers are determined for every hour of the day.
Typically, a CHP plant carries approximately the same load throughout the season: in winter, the primary product is heat, and in summer, electricity. Strong deviations are most often associated with some kind of accidents at the plant itself or at adjacent power plants in the same price zone of the wholesale market. But there are always fluctuations, and these fluctuations greatly affect the economic efficiency of the station. The required power can be taken with three boilers with a load of 50% or two with a load of 75% and see which is more efficient.
Marginality depends on market prices and the cost of electricity generation. In the market, prices can develop in such a way that it is profitable to burn fuel, but it is good to sell electricity. Or it may be that at a specific hour you need to go to the technical minimum and reduce losses. You also need to remember about the reserves and cost of fuel: the same natural gas is usually limited, and over-limit gas is noticeably more expensive, not to mention fuel oil. All this requires precise mathematical models to understand what applications to submit and how to respond to changing circumstances.
How it was done before our arrival
Practically on paper, according to not very accurate characteristics of the equipment, which have a large spread from the actual ones. Immediately after testing the equipment, at best, they will be plus or minus 2% of the fact, and a year later - plus or minus 7-8%. Tests are carried out once every five years, often less often.
The next point is that all calculations are carried out in reference fuel. In the USSR, a scheme was adopted when a certain reference fuel was considered to compare different stations on fuel oil, coal, gas, nuclear generation, and so on. It was necessary to understand the efficiency in the parrots of each generator, and the conventional fuel is the same parrot. It is determined by the calorific value of the fuel: one ton of standard fuel is approximately equal to one ton of coal. There are conversion tables for different types of fuel. For example, for brown coal, the indicators are almost two times worse. But calorie content is not associated with rubles. It's like gasoline and diesel: it's not a fact that if diesel costs 35 rubles, and the 92nd costs 32 rubles, then diesel will be more efficient in terms of calories.
The third factor is the complexity of calculations. Conventionally, based on the experience of an employee, two or three options are calculated, and more often the best mode is selected from the history of previous periods for similar loads and weather conditions. Naturally, employees believe that they choose the most optimal modes, and believe that no mathematical model will ever surpass them.
We are coming. To solve the problem, we are preparing a digital twin - a simulation model of the station. This is when, using special approaches, we simulate all technological processes for each piece of equipment, reduce steam, water and energy balances and obtain an accurate model of CHP operation.
To create a model, we use:
- The design and passport characteristics of the equipment.
- Characteristics based on the results of the latest equipment tests: every five years, the station tests and refines the characteristics of the equipment.
- Data in the APCS archives and metering systems for all available technological indicators, costs and heat and electricity generation. In particular, data from accounting systems for the supply of heat and electricity, as well as from telemechanics systems.
- Data from strip and pie charts. Yes, such analog methods of recording equipment operation parameters are still used at Russian power plants, and we are digitizing them.
- Paper logs at stations where the main parameters of the modes are constantly recorded, including those that are not recorded by the APCS sensors. The lineman walks every four hours, rewrites the testimony and writes everything down in a journal.
That is, we have restored data sets on what mode it worked in, how much fuel was supplied, what the temperature and steam consumption were, and how much thermal and electrical energy was obtained at the output. From thousands of such sets, it was necessary to collect the characteristics of each node. Fortunately, we have been able to play this Data Mining for a long time.
Describing such complex objects using mathematical models is extremely difficult. And even more difficult is to prove to the chief engineer that our model correctly calculates the operating modes of the station. Therefore, we chose the path of using specialized engineering complexes that allow us to assemble and debug a CHP model based on the design and technological characteristics of the equipment. We chose Termoflow software from the American company TermoFlex. Now Russian analogues have appeared, but at that time this particular package was the best in its class.
For each unit, its design and main technological characteristics are selected. The system allows you to describe everything in great detail both at the logical and physical levels, up to indicating the degree of deposits in the heat exchanger tubes.
As a result, the model of the thermal scheme of the station is described visually in terms of power technologists. Technologists do not understand programming, mathematics and modeling, but they can choose the design of the node, the inputs and outputs of the units and specify the parameters for them. Further, the system itself selects the most suitable parameters, and the technologist refines them so as to obtain maximum accuracy for the entire range of operating modes. We set a goal for ourselves - to ensure the accuracy of the model of 2% for the main technological parameters and achieved this.
It turned out to be not so easy to do this: the initial data were not very accurate, so for the first couple of months we went around the CHPP and manually copied the current indicators from the pressure gauges and tuned the model to the actual modes. First they made models of turbines and boilers. Each turbine and boiler was verified. To test the model, a working group was created and included representatives of the CHP.
Then we assembled all the equipment into a common scheme and tuned the CHP model as a whole. I had to work, because the archives turned out to be a lot of conflicting data. For example, we found modes with a total efficiency of 105%.
When you assemble a complete scheme, the system always considers the balanced mode: material, electrical and thermal balances are compiled. Next, we evaluate how everything in the assembly corresponds to the actual parameters of the mode according to the indicators from the devices.
What happened
As a result, we received an accurate model of the CHP process, based on the actual characteristics of the equipment and historical data. This made it possible to predict more accurately than based on test characteristics alone. The result was a simulator of the real processes of the station, a digital twin of the CHP.
In this simulator, they made it possible to conduct analysis according to βwhat ifβ¦β scenarios for given indicators. Also, this model was used to solve the problem of optimizing the operation of a real station.
It turned out to implement four optimization calculations:
- The station shift supervisor knows the heat supply schedule, the system operator commands are known, the electricity supply schedule is known: what equipment to take loads to get the maximum margin.
- Selection of the equipment composition according to the market price forecast: on a given date, taking into account the load schedule and the forecast of the outside air temperature, we determine the optimal composition of the equipment.
- Submission of applications on the market for the day ahead: when there is a composition of equipment and there is a more accurate price forecast. Calculate and apply.
- The balancing market is already within the current day, when the electric and thermal schedules are fixed, but several times a day every four hours trading is launched on the balancing market, and you can submit an application: βI ask you to load me by 5 MW.β It is necessary to find the shares of additional loading or unloading when it gives the maximum margin.
Test
For correct testing, we needed to compare the standard load modes of the station equipment with our design recommendations under the same conditions: equipment composition, load schedules and weather. For a couple of months, we chose four to six hour intervals of the day with a stable schedule. They came to the station (often at night), waited for the station to enter the regime, and only then considered it in the simulation model. If everything suited the station shift supervisor, then the operational personnel were sent to turn the valves and change the modes of the equipment.
Compared before and after performance. Peak, day and night, weekends and weekdays. In each mode, we got savings on fuel (in this problem, the margin depends on fuel consumption). Then they switched to completely new modes. I must say that at the station they quickly believed in the effectiveness of our recommendations, and towards the end of the tests, we increasingly noticed that the equipment was operating in the modes we had previously calculated.
Project summary
Facility: CHP with cross-links, 600 MW of electric power, 2 Gcal - thermal.
Team: CROC β seven people (technologists, analysts, engineers), CHPP β five people (business experts, key users, specialists).
Implementation period: 16 months.
Results:
- Automated business processes for maintaining regimes and working in the wholesale market.
- Conducted full-scale tests confirming the economic effect.
- We saved 1,2% of fuel due to the redistribution of loads during the mode.
- We saved 1% of fuel thanks to short-term planning of the composition of the equipment.
- Optimized the calculation of the levels of orders for the RSV according to the criterion of maximizing the marginal profit.
The final effect is about 4%.
The estimated payback period of the project (ROI) is 1β1,5 years.
Of course, in order to implement and test all this, we had to change many processes and work closely with both the management of the CHPP and the generating company as a whole. But the result was definitely worth it. It was possible to create a digital twin of the station, develop optimization planning procedures and get a real economic effect.
Source: habr.com