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Abstract: Within several projects we investigated grid structures and management strategies for active grids with high penetration of renewable energy resources and distributed generation (RES & DG). Those ”smart grids” should be designed and managed by model based methods, which are elaborated within these projects. Cogeneration plants (CHP) can reduce the greenhouse gas emissions by locally producing heat and electricity. The integration of thermal storage devices is suitable to get more flexibility for the cogeneration operation. If several power plants are bound to centrally managed clusters, it is called “virtual power plant”. To operate smart grids optimally, new optimisation and model reduction techniques are necessary to get rid with the complexity. There is a great potential for the optimised management of CHPs, which is not yet used. Due to the fact that electrical and thermal demands do not occur simultaneously, a thermally driven CHP cannot supply electrical peak loads when needed. With the usage of thermal storage systems it is possible to decouple electric and thermal production. We developed an optimisation method based on mixed integer linear programming (MILP) for the management of local heat supply systems with CHPs, heating boilers and thermal storages. The algorithm allows the production of thermal and electric energy with a maximal benefit. In addition to fuel and maintenance costs it is assumed that the produced electricity of the CHP is sold at dynamic prices. This developed optimisation algorithm was used for an existing local heat system with 5 CHP units of the same type. An analysis of the potential showed that about 10% increase in benefit is possible compared to a typical thermally driven CHP system under current German boundary conditions. The quality of the optimisation result depends on an accurate prognosis of the thermal load which is realised with an empiric formula fitted with measured data by a multiple regression method. The key functionality of a virtual power plant is to increase the value of the produced power by clustering different plants. The first step of the optimisation concerns the local operation of the individual power generator, the second step is to calculate the contribution to the virtual power plant. With small extensions the suggested MILP algorithm can be used for an overall EEX (European Energy Exchange) optimised management of clustered CHP systems in form of the virtual power plant. This algorithm has been used to control cogeneration plants within a distribution grid. [Copyright &y& Elsevier]

• Simulation models for water consumption and treatment in CSP plants are provided. • The integrated dynamic simulation model is validated with measured PTC plant data. • Blowdown water reuse can save 14% of water consumption without affecting LCOE. • ZBD strategy can reduce the liquid discharge significantly when using CCRO. • Lowest water use and LCOW are reached by dry cooling while LCOE is increased. Considering the importance of water in local communities and the development of Concentrated Solar Thermal Power (CSP) projects in arid regions, water footprint analysis and finding water-saving solutions towards optimized use of raw water resources in CSP plants are essential. Within this study, we present an integrated simulation tool for a comprehensive simulation of CSP plants including the water and wastewater streams, treatment units, and the cost of treatment. The models are compared to a set of measured water consumption data from a commercial CSP plant. Furthermore, strategies to reduce raw water consumption are identified and then investigated through the detailed simulation of alternative water management plans. The results show that with optimized solutions, raw water consumption can be reduced by 14% while the cost of electricity remains the same or is even slightly reduced. [ABSTRACT FROM AUTHOR]

This paper presents a simulation study to explore the flexibility of a pool of residential combined heat and power plants (cogeneration systems) coupled with thermal storages. Flexibility is identified by sending daily trigger signals (ON and OFF) of various signal lengths to a pool of 60 micro cogeneration systems over the course of one year. A randomization approach is used in order to reflect realistic system sizing based on commercially available components. Each system operates individually based on its thermal demand. The response of the pool to the external signals is evaluated. Flexibility is characterized by flexible power, flexible energy and pool regeneration time. Results show that both signal types can potentially activate a significant amount of flexible power and energy. The flexibility is highly dependent on the ambient temperature and the signal duration. [ABSTRACT FROM AUTHOR]

The influence of different control strategies and boundary conditions on heat pump system performance are investigated in this study and the trade-off between complexity and performance of different controllers is addressed. For this purpose five different control approaches for a variable speed air source heat pump in a multi family house are compared for three different use-cases. The used controls differ in complexity and the use of external input data like price and weather forecasts. The use-cases are: Constant electricity prices, time variable electricity prices and PV self-consumption. Four different rule-based controllers are compared to a convex MPC approach, presented in this work. Results show that the MPC approach reduces annual operating cost by 6–11% for constant electricity prices and 6–16% in the case of variable electricity prices. Rule-based approaches lead to cost reductions of 2–4%. MPC could increases PV self-consumption from 56% to 58% up to 64–71%. The rule base approaches are found computationally less demanding and easier to design. However fine-tuning has been considerable work and with changing boundary conditions rules had to be readjusted. It showed that increasing thermal storage without MPC is not beneficial and optimised controls are a prerequisite to benefit from increased storage sizes. [ABSTRACT FROM AUTHOR]

Heat pump (HP) units coupled to thermal storage offer flexibility in operation and hence the possibility to shift electric load. This can be used to increase PV self-consumption or optimise operation under variable electricity prices. A key question is if new sizing procedures for heat pumps, electric boilers and thermal storages are needed when heat pumps operate in a more dynamic environment, or if sizing is still determined by the thermal demand and thus sizing procedures are already well known. This is answered using structural optimisation based on mixed integer linear programming. The optimal system size of a HP, an electric back-up heater and thermal storage are calculated for 37 scenarios to investigate the impact of on-site PV, variable electricity price, space heat demand and domestic hot water demand. The results are compared to today's established sizing procedures for Germany. Results show that the thermal load profile has the strongest influence on system sizing. In most of the scenarios investigated, the established sizing procedures are sufficient. Only large PV sizes, or highly fluctuating electricity prices, create a need for lager storage. However, allowing the storage to be overheated by 10 K, the need for a larger storage only occurs in the extreme scenarios. [ABSTRACT FROM AUTHOR]

A key aspect for achieving a high-accuracy Photovoltaic (PV) power simulation, and reliable digital twins, is a detailed description of the PV system itself. However, such information is not always accurate, complete, or even available. This work presents a novel approach to learn features of unknown PV systems or subsystems using genetic algorithm optimization. Based on measured PV power, this approach learns and optimizes seven PV system parameters: nominal power, tilt and azimuth angles, albedo, irradiance and temperature dependency, and the ratio of nominal module to nominal inverter power (DC/AC ratio). By optimizing these parameters, we create a digital twin that accurately reflects the actual properties and behaviors of the unknown PV systems or subsystems. To develop this approach, on-site measured power, ambient temperature, and satellite-derived irradiance of a PV system located in south-west Germany are used. The approach proposed here achieves a mean bias error of about 10% for nominal power, 3° for azimuth and tilt angles, between 0.01%/C and 0.09%/C for temperature coefficient, and now-casts with an accuracy of around 6%. In summary, we present a new solution to parametrize and simulate PV systems accurately with limited or no previous knowledge of their properties and features. [ABSTRACT FROM AUTHOR]

• Concept to deploy distributed flexibility by rewarding system-friendly behavior. • Two-step tariff for feed-in electricity with dynamic tariff switching. • Comparison with regards to potential for influencing distributed energy systems. • Evaluation by simulated operation of a μ-CHP system following the feed-in incentive. • Drastic reduction in complexity does not diminish benefits of variable tariffs. With increasingly distributed and fluctuating generation of electricity, placing the flexibility of prosumers at the disposal of grid operators is an important step in stabilizing today's energy system. This study proposes and analyzes a concept to deploy distributed flexibility by financially rewarding system-friendly behavior. For the highest benefit, such a tariff manages to shift load to times of high generation and vice versa. The EPEX Day Ahead price was found to be a fitting price signal. Its downside however is its complexity, enhancing the need for advanced metering and communication infrastructure. In this study we introduce a two-step tariff for feed-in electricity with dynamic tariff switching times. The tariff system is implementable with a minimum of both data transfer and structural complexity. The concept is based upon the use of reliable and robust communication technologies and can be launched with commercial hardware components. We compare the dynamic two-step tariff to a state-of-the-art constant feed-in tariff, a static two-step tariff and the highly variable EPEX Day Ahead price. The tariffs are compared with regards to their potential for influencing distributed energy systems. Evaluation is done by simulated operation of a micro-CHP. Following the course of the EPEX Day Ahead price is seen as the optimal reference in the course of this study. The results show that the CHP operation optimized with regards to the introduced dynamic two-step tariff correlates strongly to the operation based on the highly variable incentive. [ABSTRACT FROM AUTHOR]