2 nd Workshop on Management and Control of Energy Supply-demand Networks with Renewables 1. Program

Transcription

1 2 nd Workshop on Management and Control of Energy Supply-demand Networks with Renewables 1 September 14, 2017 Conference room (Seminar room 1.01, 1st Floor, Waldburgstraße 19) Institute for System Dynamics (ISYS), University of Stuttgart Waldburgstraße 17/19, Stuttgart (Vaihingen), Germany Program 9:45 Oliver Sawodny (ISYS, University of Stuttgart), Kenko Uchida (Waseda University) Opening Remarks 10:00-10:30 Toru Namerikawa (Keio University) Optimal Power Demand Management among Consumers with Aggregator 10:30-11:00 Julia Sachs (Imperial College London) MUSE - Modular energy systems Simulation Environment 11:00-12:00 Hendrik Lens (Institute for Combustion and Power Plant Technology (IFK), University of Stuttgart) Special Lecture: Impact of the Energy Transition on Intrinsic Stabilizing Power System Properties Lunch 14:00-14:30 Andreas Gienger (ISYS, University of Stuttgart) Data-driven Fault Detection and Diagnosis using Graphical Models 14:30 15:00 Florian Morlock (ISYS, University of Stuttgart) Range Extension for EV by energy-optimal speed planning Coffee Break 15:30 16:00 Yasuaki Wasa (Waseda University) Dynamic Incentive Contracts between A Principal and Multiple Agents in Energy Transaction Systems 16:00-16:30 Kenji Hirata (Nagaoka University of Technology) Strategic Behavior and Market Power of Aggregators in Energy Demand Networks 16:30 17:00 Takeshi Hatanaka (Tokyo Institute of Technology) A CPS Design for HVAC Optimization/Control and Its Demonstration on EnergyPlus Closing Remarks 1 1 st Workshop at Waseda University, Tokyo, Japan on March 2, 2017 ( /Zeitz)

2 Abstracts Toru Namerikawa (Keio University) Optimal Power Demand Management among Consumers with Aggregator According to the deregulation of an electricity market, both power consumers and generators are willing to participate in energy market trading. This result indicates that the power adjustment becomes more efficient in the future power grid since its adjustment is conducted from both power supply and demand sides. In addition, as the number of market players including consumers increases, an aggregator, who is a mediator in the market trading between consumers and the market operator, is expected to have an important role in the efficient electricity market trading. This talk proposes a novel distributed power demand management algorithm in megawatt trading considering state and control constraint conditions. In order to derive the optimal power reduction of each consumer in a distributed manner and to satisfy the constraint conditions regarding states and control inputs at any time, the proposed method integrates market trading and the control input decision problem. Specifically, this method derives the optimal power reduction of each consumer based on the market trading among consumers and the aggregator, while the control inputs to their demand control devices are determined by solving a quadratic programming problem. Consequently, this method does not require consumers to reveal their private functions such as disutility functions or cost functions of their standby generators. In addition, this method determines these control inputs with constraint conditions regarding the states and inputs through the above quadratic programming problem. Therefore, the proposed method achieves to derive the optimal power reduction of consumers with each decision variable satisfying its constraint condition at any time. Julia Sachs (Imperial College London) MUSE - Modular energy systems Simulation Environment MUSE, the Modular energy systems Simulation Environment, is a modelling environment enabling the generation of plausible scenarios of technological transition of the energy system. One of the distinguishing features of MUSE is the implementation of investors view on energy system transitions. Investment and activity in each sector module are driven by the perceived utility of those decisions. Most models either use a central planning approach to suggest optimal energy system changes, or use a single investment metric across the economy. This modular approach, embedded in a partial equilibrium model of the energy, allows the modelling of the energy systems where the specific drivers to investments and operations (i.e. production and emissions levels) are tailored to represent each specific energy sector as well as the motivations of sector-specific agents. A particular focus will be on the building sector module (BSM) in MUSE which belongs to the demand sectors. The purpose of the BSM is to project future demand for all end-uses on a global scale, disaggregated into regions and temporally resolved into time slices and to determine the investment in new assets. Energy-related investment decisions in the residential sector are heterogeneous in the sense that they are not based on one specific criteria and depend highly on the peoples behavior. Therefore, advanced modelling methods are needed to improve the characterisation of energy-technology diffusion, capture diversity in decision-making, and enable the inclusion of economic, comfort, environmental as well as social aspects. The BSM includes an advanced model to capture several aspects of people's behavior concerning energy-related investment decisions in the building sector using an agent-based approach. Hendrik Lens (IFK, University of Stuttgart) Special Lecture: Impact of the Energy Transition on Intrinsic Stabilizing Power System Properties One of the fundamental control challenges in power systems of whatever size is keeping the balance

3 between generation and demand. Electricity balancing in Europe is - similarly to other power systems - based on several cascaded control loops such as automatic frequency containment reserve (afcr, also known as primary control) and automatic frequency restoration reserve (afrr, also known as secondary control). This or similar balancing mechanisms have shown remarkable performance and reliability for very large interconnected power systems as in continental Europe. Classically, electricity balancing has been performed by conventional power plants. In the course of the transition of the energy system in Germany, this is currently shifting towards other technologies such as - to name a few examples - battery systems, power to heat, and virtual power plants, which are composed of a high number of smaller, decentralized plants. Electricity balancing by renewables is not yet common practice but technically feasible. The increasing complexity of balancing due to the high number and the high volatility of services providers may pose some challenges but, in general, can be considered as manageable. However, apart from active balancing, other, intrinsic properties of synchronous generators and consumers are important for system stability. Among those are inertia, reactive power, short-circuit power, and the self-regulating effect. This talk provides an overview and highlights the importance of some of those intrinsic properties. The impact of changes in these properties on the dynamic behavior and the stability of the power system is shown. Finally, some particular issues that arise when discussing possible solutions will be presented in an outlook. Andreas Gienger (ISYS, University of Stuttgart) Data-driven Fault Detection and Diagnosis using Graphical Models In a variety of engineering systems like production plants and energy systems, faults or malfunctions may affect the system performance and functionality. For this reason, the determination and prediction of the system condition as well as the detection of faults play an important role in engineering systems. In this talk, a data-driven approach for fault detection and diagnosis is presented which is based on a graphical model. The graphical model is used to represent the qualitative as well as quantitative relationships among large amount of different sensor data and derives an input-output model of the underlying physical process. The model considers both linear as well as nonlinear relationships using Gaussian processes. Based on the model, a methodology for anomaly and fault detection is presented. The approach is capable of detecting known faults integrated in the model as well as faults detected by the anomaly detection. The effectiveness of the approach is illustrated for a real world air-intake system. Florian Morlock (ISYS, University of Stuttgart) Range Extension for EV by energy-optimal speed planning Throughout the last decade, piloted and (semi-) autonomous driving emerged as major field of research in automotive engineering and control of longitudinal vehicle dynamics by intelligent cruise controllers has long become state of the art. However, most systems neither consider energy efficiency nor adapt to driver's desires in longitudinal dynamics. This talk focuses on a simple, yet accurate model for longitudinal driving characteristics that is designed for use in longitudinal vehicle control in an intelligent cruise controller. Free travel and car-following characteristics of drivers are considered and model parameters are identified from measurement data. The proposed model is used to compute reference speed trajectories reflecting the driver's desires in longitudinal dynamics that are evaluated by comparison to measured data. Potential for energy savings by small variation of computed speed trajectories is assessed using sequential quadratic programming and dynamic programming frameworks.

4 Yasuaki Wasa (Waseda University) Dynamic Incentive Contracts between A Principal and Multiple Agents in Energy Transaction Systems This talk investigates an optimal dynamic incentive contracts between a principal and multiple agents with local controllers in energy transaction systems. We first propose a novel optimal incentive contract under moral hazard in continuous-time Markov models. Consequently, by using an external design parameter manipulating agents' control input indirectly, the principal can give each agent an optimal incentive plan called salary function. We next show that the optimization problem of the external design parameter can be reduced to the synthesis problem of optimal incentive contracts. In other words, by using the proposed dynamic contract, the principal can lead agents' behavior with action constraints to an arbitrary Nash equilibrium. We finally discuss possibility, interpretation and limitation of our proposed approach through typical cases. Kenji Hirata (Nagaoka University of Technology) Strategic Behavior and Market Power of Aggregators in Energy Demand Networks This talk considers optimization problems of energy demand networks including aggregators and investigates strategic behavior of the aggregators. The energy demand network including aggregators will be optimized through pricing. Under this optimization process, the aggregator acts as intermediate between energy supply sources and a large number of consumers. From the consumer's point of view, the aggregator is expected to have enough negotiation power on behalf of the consumers. We investigate strategic behavior of the aggregators and suppose that the aggregator will try to pursue the benefit as well as market power by choosing the design parameter in its cost function. The strategic decision making by the aggregators could provide useful insights in qualitative analysis of the energy demand network, and the results of numerical example indicate that, for example, oligopoly by the aggregator may not be beneficial to the consumers. Takeshi Hatanaka (Tokyo Institute of Technology) A CPS Design for HVAC Optimization/Control and Its Demonstration on EnergyPlus In this talk, we address a CPS (Cyber-Physical System) design for building HVAC (Heating, Ventilation and Air-Conditioning) optimization and control. We first formulate a set-point optimization problem for the HVAC systems and derive its dynamic solution process which is regarded as dynamics in a cyber world. We then design physical dynamics including a learning process to produce estimates of uncertain factors to be fed back to the optimization. Both cyber and physical dynamics are then proved to be passive, and they are interconnected with each other via feedback interconnection. The resulting CPS is then shown to ensure closed-loop stability and asymptotic optimality based on the passivity preservation property. Moreover, the resulting process is shown to be plugged into a solution process of a higher-level optimization, which results in a novel three-layer hierarchical/distributed control architecture consisting of community-level global optimization, building-level local optimization and physical dynamics. We finally demonstrate the present CPS using a model of our own building through a real-time HVAC simulator developed based on various software including EnergyPlus, MATLAB, BRCM toolbox, SketchUp and BCVTB.

5 Chairs and Authors O. Sawodny K. Uchida A. Gienger T. Hatanaka K. Hirata H. Lens F. Morlock T. Namerikawa J. Sachs Y. Wasa