Case Kalasatama Sample Clauses

Case Kalasatama. The simulation model for electrical energy storage created for the Kalasatama case has been validated against data collected from the Environment Building. The early operation data from Suvilahti XXXX at Kalasatama was also used to compare the simulation and operation results and the analysis of the results are reported in Deliverable 4.1. The Suvilahti XXXX provided data between 1.- 31.8.2016 when the system was operating in frequency control and voltage support in reactive power compensation. The Suvilahti XXXX is shown in Figure 2. The energy system at the Environment Building is shown in Figure 3. A 60 kW photovoltaic panels, 45 kWh/90 kW battery energy storage and electric vehicle charger are installed at the system. The energy system at the Environment Building was running in holistic optimization for two weeks to collect data for validation purposes. The battery energy storage was utilized depending on spot prices and the rate of PV production with respect to consumption of Environment Building. The test run took place between 13.6.-26.6.2016. The study of the Environment building addresses the following study cases: • Validation of the CITYOPT model of battery energy storages • Optimization of the energy consumption of the Environment building • Optimization of the economic benefits of the XXXX in Environment building • Collection of information from the use case and behaviour of the XXXX The metrics of such energy systems that are analysed within the CITYOPT project are monetary compensation, cost structure, CO2 reduction (kgCO2), energy savings, XXXX usability and degradation of battery’s energy capacity. The study of the Suvilahti XXXX addresses the following study cases: • Frequency control • Peak power shaving • Voltage support • Energy time shifting using the Suvilahti solar power plant to charge the batteries The primary application of the Suvilahti XXXX is the frequency control of the power system. Frequency control is a function of power and system frequency. Frequency control curve visualizes the relation of the power and frequency. Fingrid Oyj is given a freedom to change this frequency control curve, the battery energy storage’s main research aspect of the agreement. An example curve for frequency regulation is shown in Figure 4. Xxxxx has also agreed to offer peak power shaving and voltage support to the distribution system operator (DSO) Xxxxx Electricity Network Ltd. Voltage is a local attribute, which can be supported by injecting r...
Sample 1
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Case Kalasatama. Each of the four GA-optimisation sets were run for 2 hours on average and resulted to 200 - 500 scenarios. The best scenarios generated for each optimisation set are shown in Table 10. An additional scenario was simulated for each building type where the same panel tilt angle was used but all photovoltaic capacity was put to the southern direction. In all cases this showed to give better results in cost balance and CO2 emissions than the optimal (pareto) cases found by the GA- algorithm. However, the GA-algorithm would have eventually found this out with sufficient number of iterations. This points out the important aspect of the user being able to consider obvious/potential cases when setting up an optimisation problem in order to save processing time and calculation capacity. Similar situation was with the battery capacity which was zero kilowatt-hours (0 kWh) in all of the best optimisation cases. This was most likely due to the high investment costs with respect to minimal savings in bought electricity and could have been deduced without the simulation. The residential and the centralized buildings did not produce excess solar electricity at any moment due to limited available rooftop area for energy production. This in turn causes the battery to be unnecessary, at least for storing excess photovoltaic production. The idle time in solar production optimization in one-year period is long and hence the battery could have other use cases to add its value and viability. In solar optimization the battery is activated during the sunny months and it can be concluded that other functionalities such as back-up power, peak shaving and frequency containment reserves could be introduced. Table 8. Results from genetic optimization of the planning tool for Östersundom district. Genetic optimization Energy Costs (€) CO2 emissions (kg) PV area total (m2) Solar collector area total (m2) Storage volume total (m3) CHP capacity heat (kW) CHP capacity electricity (kW) 1 22 592 647 61 675 438 188 659 26 710 219 990 18 274 6 091 2 23 283 392 61 951 194 164 085 60 000 000 000 19 885 6 628 3 23 172 237 62 193 769 173 301 64 457 204 704 17 815 5 938 4 22 814 496 62 660 676 187 287 28 041 213 164 19 766 6 589 5 22 971 166 62 924 171 166 132 38 000 000 000 19 718 6 573 6 22 601 141 63 183 743 162 528 42 780 205 448 11 625 3 875 7 22 620 660 63 729 837 158 597 43 793 198 512 11 669 3 890 8 22 684 947 63 732 689 142 379 43 122 210 563 11 346 3 782 9 22 736 977 63 618 948 164 736 67...
Sample 1
Case Kalasatama. The optimisation runs showed that finding the optimal scenario might depend on simulation capacity and time. However, this can be assisted by formulating exceptions or setting proper boundaries that help the optimisation algorithm to rule out unnecessary cases. The Kalasatama case has two objective functions, cost balance and CO2 emissions, from which the optimisation algorithm might have difficulties finding the “best” scenarios unless the user is able to define the weight of these factors with respect to each other. However, with enough calculation capacity and good optimisation settings, the CITYOPT Planning Tool would be powerful tool finding good solutions in similar cases as the case Kalasatama where the system is specific and the boundaries of the parameters are well defined. The simulation results would help energy companies as well as system providers to find more feasible ways to operate existing systems or find new business opportunities for their products. Also, simulation results could provide information or evidence for politicians to adjust incentives or market directives in a way that allows intermittent storage of renewable energy production to become more popular. One example for this would be to recognize electricity storages as individual components of energy systems and create market rules and adjust the tax treatment to support storage as an attractive option to be combined with renewable energy production. Once the model has been made, new analyses are easily made in the future in case some of the parameters such as investment or electricity prices change which might affect the outcomes radically. Future control logics to be examined would involve further options (capacity reserve market, frequency reserve market and spot-price trading) for the use of battery technology which then could make it more economically profitable. Also further intelligence need to be included in the model such as spot-price optimisation through prediction or sell/buy agent algorithms to comprehensively analyse attractive scenarios.
Sample 1

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