Utilizing load shifting potentials of especially cross sectoral energy systems to improve the integration of renewable energy sources in operational processes is crucial for sustainable management. This is illustrated in the case study of a hospital where the operation of various energy systems is optimized for economic efficiency. For each system, the flexibility potential of the resulting optimal operation is analyzed. Also, the influence of dynamic electricity prices are considered and the effects on the total operational costs discussed. Results indicate that cross-sectoral energy systems facilitate operational cost reductions contingent on implementing intelligent remote-control mechanisms. Additionally, the research highlights the major role of storage systems in augmenting system flexibility, primarily through their inherent load-shifting capabilities.

The increased use of renewable and decentralized energy sources for the power supply leads to various challenges in grid planning and grid operation. Two essential challenges are the need to deal with an ever-increasing number of controllable devices and, hence, flexibility in grid operation, along with the need to utilize existing power system infrastructures highly. The resulting complexity of power system operational planning and operation can no longer be covered by system operators without having powerful decision support systems and automized control schemes. (Mathematical) Optimization approaches allow specific support of operators in dealing with the increased demands, e.g., in the fields of congestion management, reactive power/voltage control and exchange, as well as innovative operational schemes such as curative system operation.

Iqony Energies is one of the largest district heating suppliers in Germany. Due to the changes in recent years and the increasing volatility on the energy markets, the previous operation mode of our combined heat and power plants according to ability and condition is becoming less and less attractive. For this reason, it was necessary to establish a new mode of operation based on the hourly prices on the energy markets. To achieve this, we at Iqony Energies have developed a complex tool to optimally control our combined heat and power plants from an economic point of view and thus keep the heat generation costs as low as possible. The development of this tool included creating digital twins of our plants, forecasting the heat demand in our district heating networks over the next few days and economically optimizing the operation schedules of our heating plants. The essence of this solution is that after an initial effort, the entire process, including the creation and marketing of a schedule, is fully automated. I will give you an insight into this tool and how it works, how we came up with this solution and outline some of the challenges we have faced and will have to face in the future.

In the domains of Energy Management and Mathematical Optimization, specialized expertise is crucial for their effective navigation. Small and Medium Enterprises (SMEs), often lacking personnel with specific training and advanced skills in these areas, face challenges in fully capitalizing on the potential benefits. Consequently, SMEs experience diminished operational efficiency, increased costs, and missed cost-reduction opportunities.

Addressing this pervasive issue, EcoPlanet created an innovative AI system that can perform optimizations and energy management following simple conversations with users. Implementation of this approach yields significant enhancements in operational efficiency of the first and second order, accompanied by positive side-effects that resonate throughout various operational facets, transforming overall business performance.

A distinctive feature of EcoPlanet’s solution lies in its role as an enabler, democratizing Energy Management and Optimization capabilities for SMEs. The integration of our AI system lowers the entry barrier, empowering SMEs to harness sophisticated energy optimization without the need for specialized personnel. This paradigm shift not only resolves immediate challenges for SMEs but aligns with a broader vision of fostering sustainability and efficiency within businesses. Through accessible and user-friendly interactions, EcoPlanet provides SMEs with tools and insights necessary to navigate the complexities of energy resource management, contributing to a more resilient and resource-efficient business landscape. This research thus showcases the potential of advanced AI systems in revolutionizing the accessibility and effectiveness of energy management practices, particularly for SMEs.

Optimization is a key component of energy infrastructure planning to meet global climate goals on time and at a reasonable cost. In this presentation, OET will present strategies for state-of-the-art integrated energy system planning. This includes the optimization of energy production, transmission and storage as well as electrification strategies in relevant energy sectors on a high temporal and spatial scale. We show how the open-source PyPSA (Python for Power System Analysis) ecosystem stands out through a highly configurable and resource-efficient modelling of energy system. Among many other features, it enables to calculate renewable energy installation potentials, to distribute energy within the limitations of the power and gas grids, the inclusion of assumptions about the energy system of today and tomorrow, and the formulation of abstract techno-economic boundary conditions. One of its main advantages is the fast, efficient and flexible solver interface based on the new open-source Python toolbox “linopy”. In combination with Gurobi, it enables the efficient and user-friendly handling of large programs with millions of variables. On this basis, the PyPSA ecosystem can help to realize highly optimized energy infrastructure designs that support a sustainable future.

In the dynamic landscape of the energy industry, optimization plays a crucial role in enhancing efficiency, sustainability, and cost-effectiveness. Gurobi, a leading provider of optimization software, stands at the forefront of supporting optimization efforts within the energy sector. This presentation explores how Gurobi’s flexible application architectures empower the development of tailored solutions to address intricate energy challenges efficiently. Moreover, it delves into the importance of expert support provided by Gurobi, ensuring users can harness the full potential of optimization techniques in optimizing various aspects of energy systems. Additionally, the presentation highlights Gurobi’s commitment to energy-related research, driving innovation and advancing optimization algorithms to meet evolving industry needs.

A major problem of a complex IT structure in bigger companies is a proper data management between the individual applications and holistic reporting in all management levels.
With the data management Tool of ABD GmbH new data flows can be implemented fast and adopted quite easily. This should be helpful to collect data from all needed internal or external sources during an implementation of a new optimization tool in the customers IT system. The results of the optimization can be sent back to core systems for adjustments in the operating are or to a comprehensive reporting of the relevant data for a better process transparency.
The tool provides a save framework with automatized documentation for process experts with lower IT skills to adjust existing data flows by themselves and offer great flexibility for IT experts with nearly no boundaries by using even Python snippets or additional plugins.
The main advantage are the reduction of implementation time and the flexibility for changes with low effort during the whole lifetime so the individual focus can remain on the own core application.

Electric distribution networks are undergoing the largest and fastest change any current planner has ever experienced. Change is driven by a major increase in demand due to electrification (primarily electric vehicles and heat pumps) and the integration of distributed energy resources (primarily rooftop solar PV, batteries, and dispatchable or flexible demand). The magnitude of the change will require major upgrades to the electric distribution infrastructure involving the replacement or addition of wires and transformers. The need for upgrades will vastly vary by location. Non-wire alternative solutions (primarily distributed energy resources) will have the potential to reduce or avoid upgrade costs in some locations and under some scenarios. encoord’s Scenario Analysis Interface for Energy Systems (SAInt) is a planning software that is uniquely positioned to address this new and urgent challenge. SAInt combines traditional planning methods from bulk power systems and local distribution networks in an integrated planning solution for electric distribution planners. They can simulate their local networks with detailed representation of the variable and/or dispatchable distributed energy resources. In addition, they can run detailed optimization models to evaluate the optimal operation of the distributed energy resources and quantify their ability to displace or reduce infrastructure upgrade costs, all while considering the spatial and temporal variability of the growing electricity demand and potential power flow constraints of an aging infrastructure. This presentation will highlight how electric distribution planners can use optimization models to plan for the largest and fastest change they have ever experienced.

The electric power system is a critical infrastructure and crucial in modern societies. With decarbonization, decentralization, digitization, and sector coupling, the dependency on a reliable and resilient power system has increased even more, as it is becoming the linchpin of the future energy system. On the other hand, interconnections between the physical power system and information and communication technologies raise new vulnerabilities in the evolving cyber-physical energy system. Identifying these vulnerabilities is a crucial step towards enhancing the resilience of energy systems against adverse events. A prominent approach to detect these vulnerabilities is mathematical bilevel optimization, featuring an attacker at the upper level and an optimal power flow (OPF) at the lower level. Reformulating the bilevel optimization models of different OPF formulations into a mixed-integer linear program allows for structural comparison of these approaches in a vulnerability assessment context. In this talk, we give an overview of possible vulnerabilities in cyber-physical energy systems and describe our bilevel analysis for power systems, discussing the insights we gained.

Thousands of assets are connected to the virtual power plant of e2m and are marketed on different markets – wholesale and balancing markets. Optimization is used to determine best options for maximizing profit of available flexibility of these assets.
Along two use cases, this presentation shows how optimization is applied at energy2market to market energy from biogas power plants, battery energy storage systems and co-locations of volatile energy sources and battery storages. Moreover, this talk reveals insights about the application infrastructure that allows to handle decentralized power plants at scale.

Climate change is a global threat causing catastrophic events. The emission of greenhouse gases caused by the heating sector is a driving factor for the global temperature increase. Expanding district heating networks and the optimal design of energy converters and storages in a thermal network can support the transition to a renewable heating sector. Moreover, the increased availability of open data allows the creation of detailed digital models for demand estimations. We present a workflow that builds a digital district model from open data, evaluates suitability for a district heating network, and expands and connects buildings into a coherent district heating network. For further analysis, we developed a mixed-integer linear programming model for the optimal design of energy converters and storages supplying the so-created district heating network. The model selects, designs, and operates energy converters and storage systems connected to the network. The method was applied to a case study in a specified district in Frankfurt am Main, Germany. For roughly a third of our study area, the supply by a district heating network is proposed. The region could be supplied by a thermal network and renewable energy generation with total system costs below 16 ct/KWh.

In order to achieve the political goals of decarbonization, heat transition, climate neutrality and CO2 reduction, all energy suppliers will have to restructure their supply systems, which could potentially result in immense costs.
The BelVis ResOpt IT solution from Aachen-based IT service provider KISTERS AG is an important and reliable decision-making aid that shows companies cost- and CO2-optimized options for action and future scenarios so that energy supply companies can put their investments on a secure footing in terms of the energy transition.
Firstly, it enables them to quickly find out which options for reducing CO2 emissions in their current energy system can already be realized in the short term, e.g. by changing the operating modes of producers, and secondly, which paths and investments make sense for converting their system in the long term.
Energy supply companies can run through various scenarios of their own system in BelVis ResOpt and thus answer questions such as ” What additional costs arise with a 30% reduction in CO2?”.
In the presentation, a Pareto front of costs to CO2 is calculated using a MILP demo model.

Modern low-temperature district heating networks (DHNs) are considered a key factor in enabling zero-emission heat supply because of their ability to connect a variety of different renewable and waste heat sources and to provide heat to districts and entire cities.
Today, pipe routing and heat producer design for DHNs typically focuses on simplified approaches that relax or do not consider the nonlinear nature of the design problem. As a result, these models fail to evaluate the operating temperature required in a low-temperature network to maximize energy efficiency while ensuring heat demand satisfaction. In addition, these approaches fail to evaluate flow distribution in networks with multiple producers or with flow loops. Some approaches take nonlinearities into account, but use optimization routines that are either not scalable to large networks or not reliable in obtaining an optimal solution (heuristic approaches).

To support the design of low temperature and low carbon DHNs, a recently developed automated design approach (PATHOPT) is presented. By solving the binary pipe routing problem as a nonlinear topology optimization problem and using an adjoint-based optimization method, this approach remains scalable for large-scale applications. The approach uses multi-objective optimization to balance CO2 emissions and network costs, and is based on a detailed physical model. In addition to the optimization method, we present case study results of optimal DHN designs for cities in Belgium.

Energy system optimization has become a very common method of designing energy systems that are both more sustainable and also cheaper to operate than in the past. This can be achieved by introducing large quantities of solar energy into the energy systems either in form of solarthermal heat or as electricity from photovoltaic. Both conversion technologies have in common, that the availability of solar energy is much higher during the summer when the demand for energy is often low. The ongoing roll-out of heat pumps as a form of converting electricity into heat with very high efficiencies intensifies this discrepancy. A way of dealing with this problem is the use of either electrical or thermal storages that store energy for a whole season. A typical technical representation are ice storages for example.
The optimization of the design and operation of theses kinds of storages is particularly challenging, because at least one year of operation has to be optimized simultaneously, which often leads to very high calculation times. In this talk we will present a way of optimizing different kinds of long-term time coupling constraints in a reasonable time frame. This is done by a combination of downsampling and relaxation of the original problem and using a multi-stage heuristic in order to find the best design and operation for a seasonal hydrogen storage. The algorithm is integrated in the commercial energy optimization toolbox TOP-Energy and can also be applied to other constraints like peak power prices, grid usage full load hours or upper limits for CO2-emissions.