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Power providers are facing multiple challenges when matching supply and demand in an ever-changing environment. Beyond using existing demand forecasts to optimally operate existing plants and trade energy to ensure delivering the right amount of electricity and heat, additional challenges exist. These include political and ecological developments that demand a shift from fossil primary energy towards renewable, green energy sources such as wind, solar, hydrogen, etc. Also, energy storage plays an increasingly important role in leveling out the given supply variations of several types of green energy.
For planning the optimal operating mode of the existing supply network, as well as evaluating scenarios involving new facilities, it is of paramount importance to consider all relevant constraints and parameters. Generally, those are of technical, engineering, financial, and political nature. Such planning scenarios require careful modeling of all relevant factors, ideally in a comprehensive mathematical optimization model.
The Solution
Based on the long-lasting experience of its founders, proven in several successful projects, ENOSYS built the EIP Energy Investment Planner which helps energy providers such as municipal power providers in optimizing their processes and resources and consequently maximizing their profits while meeting the constraints. This is achieved by applying state-of-the art mathematical modeling and optimization, leading to informed decisions pertaining to tactical planning and strategic investments.
The EIP Energy Investment Planner is based on a flexible mathematical optimization model, formulated and implemented in GAMS. Depending on the input data, a linear (linear program, LP) or mixed-integer linear (mixed-integer linear program, MILP) mathematical optimization model is generated and solved with the aim of maximizing total profit over the entire planning horizon within the given revenue and cost structure. While existing constraints such as CO2 emission limits, number of yearly starts or operating hours of facilities, minimum heat supply, primary and secondary energy prices, investment costs and subsidies for new facilities, etc. are considered, the generated GAMS model dynamically adapts to the provided data, resulting in just the right model complexity.
In the graphical, interactive, web-based user interface shown in Figure 1, users define the topology of their respective energy networks by intuitively placing and connecting components on the canvas, followed by filling in forms with the required parameters, without needing to delve into the details of the mathematical formulation. The optimization results are presented in graphs and tables and can be conveniently exported for individual further processing and reporting (Figures 2 and 3). Additionally, in the solution view, various scenarios can be compared right in the interactive GUI for easily conducting What-If analyses (Figure 4). Note that – while more language versions are in development – the user interface is presently available in German only.
Fig 2. Yearly CHP (combined heat and power) production and usage (example)Fig 3. Electricity production and usage report (example)Fig 4. Comparing costs and revenues of three scenarios (example)
Optimization Model Complexity
Being a tool for a variety of planning applications in the energy industry, the EIP Energy Investment Planner needs to be ready and fit for a wide range of model sizes and complexities. This ranges from planning a comparatively simple pumped-storage hydropower station with just four processes operating on two resources, to municipal electricity and heat networks with multiple power generation alternatives, emission control, etc. The latter can easily go well over a dozen processes and 5 resources or more, which can further increase when modeling more complex constraints such as green energy-quotas, H2-Electrolysis, etc.
Certain technical or business constraints will directly impact model complexity: imposing a maximum number of starts for instance, will require solving a mixed-integer linear program (MILP), which is significantly more time consuming than solving a purely continuous linear program (LP). In the testing phase, these “integrality properties” of the created model can be relaxed for quicker model development and prototyping.
Finally, the planning horizon and time granularity strongly influence model sizes and hence the required time and computing power to solve a model. Depending on the application scenario, time horizons vary from a year to several decades, while typical individual time intervals will range from 15 minutes up to a day. Typically, this results in tens of thousands of time periods.
Architecture
The EIP Energy Investment Planner is a Software-as-a-Service product. While the GUI and the projects’ database are hosted and are operating on servers in the European Union (Germany), the optimization processes are typically executed via the GAMS Engine SaaS, which provides practically limitless horizontal scaling because it can start as many parallel jobs as required. To cater to specific customer needs, the EIP can optionally use a dedicated optimization server hosted in the European Union.
Being a Software-as-a-Service product, the EIP Energy Investment Planner is licensed on a yearly basis. Licenses typically include a computation time-quota and come with everything you need to start optimizing. There is no need to separately license GAMS or an optimization engine. Realizing the wide range of planning tasks the EIP is suitable for, licenses can be upgraded at any time. Please get in touch at team@enosys.ltd
for discussing your specific needs.
About ENOSYS
ENOSYS Energy Optimization Systems Ltd strives for creating value for companies, their clients, and our environment by making innovative mathematical models accessible to energy producers and project developers on a licensing basis without the customer needing to master the details of the mathematical formulation.
Our Software-as-a-service product, the EIP Energy Investment Planner, enables better decision-making for key aspects of the energy transition, including strategic investment planning and tactical planning. It reduces costs and time for modeling and/or decision-making, enhances returns, and secures company viability.
