In the regime of incentive-based autonomous demand response, time dependent prices are typically used to serve as signals from a system operator to consumers. However, this approach has been shown to be problematic from various perspectives. We clarify these shortcomings in a geometric way and thereby motivate the use of power signals instead of price signals. The main contribution of this paper consists of demonstrating in a standard setting that power tracking signals can control flexibilities more efficiently than real-time price signals. For comparison by simulation, German renewable energy production and German standard load profiles are used for daily production and demand profiles, respectively. As for flexibility, an energy storage system with realistic efficiencies is considered. Most critically, the new approach is able to induce consumptions on the demand side that real-time pricing is unable to induce. Moreover, the pricing approach is outperformed with regards to imbalance energy, peak consumption, storage variation, and storage losses without the need for additional communication or computation efforts. It is further shown that the advantages of the optimal power tracking approach compared to the pricing approach increase with the extent of the flexibility. The results indicate that autonomous flexibility control by optimal power tracking is able to integrate renewable energy production efficiently, has additional benefits, and the potential for enhancements. The latter include data uncertainties, systems of flexibilities, and economic implementation.
In contrast to fossil energy sources, the supply by renewable energy sources likewind and photovoltaics can not be controlled. Therefore, flexibilities on the demandside of the electric power grid, like electro-chemical energy storage systems, are usedincreasingly to match electric supply and demand at all times. To control those flex-ibilities, we consider two algorithms that both lead to linear programming problems.These are solved autonomously on the demand side, i.e., by household computers.In the classic approach, an energy price signal is sent by the electric utility to thehouseholds, which, in turn, optimize the cost of consumption within their constraints.Instead of an energy price signal, we claim that an appropriate power signal that istracked in L1-norm as close as possible by the household has favorable character-istics. We argue that an interior point of the household’s feasibility region is neveran optimal price-based point but can result in a L1-norm optimal point. Thus, pricesignals can not parametrize the complete feasibility region which may not lead to anoptimal allocation of consumption.We compare the price and power tracking algorithms over a year on the base ofone-day optimizations regarding different information settings and using a large dataset of daily household load profiles. The computational task constitutes an embarrassingly parallel problem. To this end, the performance of the two parallel computation frameworks DEF [1] and Ray [2] are investigated. The Ray framework is used to run the Python applications locally on several cores. With the DEF frameworkwe execute our Python routines parallelly in a cloud. All in all, the results providean understanding of when which computation framework and autonomous algorithmwill outperform the other.
The electricity demand due to the increasing number of EVs presents new challenges for the operation of the electricity network, especially for the distribution grids. The existing grid infrastructure may not be sufficient to meet the new demands imposed by the integration of EVs. Thus, EV charging may possibly lead to reliability and stability issues, especially during the peak demand periods. Demand side management (DSM) is a potential and promising approach for mitigation of the resulting impacts. In this work, we developed an autonomous DSM strategy for optimal charging of EVs to minimize the charging cost and we conducted a simulation study to evaluate the impacts to the grid operation. The proposed approach only requires a one way communicated incentive. Real profiles from an Austrian study on mobility behavior are used to simulate the usage of the EVs. Furthermore, real smart meter data are used to simulate the household base load profiles and a real low voltage grid topology is considered in the load flow simulation. Day-ahead electricity stock market prices are used as the incentive to drive the optimization. The results for the optimum charging strategy is determined and compared to uncontrolled EV charging. The results for the optimum charging strategy show a potential cost saving of about 30.8% compared to uncontrolled EV charging. Although autonomous DSM of EVs achieves a shift of load as pursued, distribution grid operation may be substantially affected by it. We show that in the case of real time price driven operation, voltage drops and elevated peak to average powers result from the coincident charging of vehicles during favourable time slots.
Verbraucherseitige Laststeuerung (Demand Side Management – DSM) wird als ein möglicher Ansatz betrachtet, um die Auswirkungen des Ausbaus von fluktuierenden Erneuerbaren im Stromnetz auszugleichen. Sollen viele verteilte Energiesysteme damit angesprochen werden, stellen zentralistische Ansätze dabei hohe Anforderungen an die Kommunikationsinfrastruktur. Als Alternative wird vielfach eine autonome Laststeuerung (ADSM) mit anreizbasierter Optimierung direkt auf dem Verbrauchergerät betrachtet. Dabei kann die Anreizfunktion mittels unidirektionaler Kommunikation übertragen werden.
Am Forschungszentrum Energie der Fachhochschule Vorarlberg wurden in den letzten Jahren Algorithmen und Prototypen für den Einsatz von ADSM auf verschiedensten verteilten Energiespeichern im elektrischen Stromnetz entwickelt. Dabei werden sowohl thermische Energiespeicher (z. B. Haushalts-Warmwasserspeicher) als auch elektrochemische Speicher (z. B. Batteriespeichersysteme oder Elektroautos) betrachtet. Außerdem werden die Auswirkungen solcher Systeme auf das elektrische Verteilnetz untersucht. Dieser Artikel gibt einen Überblick über die entwickelten Methoden und Ergebnisse aus diesem Forschungsfeld mit dem Ziel, ein weitreichendes Verständnis für die Chancen und Grenzen des ADSM zu schaffen.
Hot water heat pumps are well suited for demand side management, as the heat pump market faced a rapid growth in the past years with the trend to decentralized domestic hot water use. Sales were accelerated through wants and needs of energy conservation, energy efficiency, and less restrictive rules regarding Legionella. While in literature the model predictive control potential for heat pumps is commonly shown in simulations, the share of experimental studies is relatively low. To this day, experimental studies considering solely domestic hot water use are not available. In this paper, the realistic achievable model predictive control potential of a hot water heat pump is compared to the standard hysteresis control, to provide an experimental proof. We show for the first time, how state-of-the-art approaches (model predictive control, system identification, live state estimation, and demand prediction) can be transferred from electric hot water heaters to hot water heat pumps, combined, and implemented into a real-world hot water heat pump setup. The optimization approach, embedded in a realistic experimental setting, leads to a decrease in electric energy demand and cost per unit electricity by approximately 12% and 14%, respectively. Further, an increase in efficiency by approximately 13% has been achieved.
Flexibility estimation is the first step necessary to incorporate building energy systems into demand side management programs. We extend a known method for temporal flexibility estimation from literature to a real-world residential heat pump system, solely based on historical cloud data. The method proposed relies on robust simplifications and estimates employing process knowledge, energy balances and manufacturer's information. Resulting forced and delayed temporal flexibility, covering both domestic hot water and space heating demands as constraints, allows to derive a flexibility range for the heat pump system. The resulting temporal flexibility lay within the range of 24 minutes and 6 hours for forced and delayed flexibility, respectively. This range provides new insights into the system's behaviour and is the basis for estimating power and energy flexibility - the first step necessary to incorporate building energy systems into demand side management programs.
Increasing electric vehicle penetration leads to undesirable peaks in power if no proper coordination in charging is implemented. We tested the feasibility of electric vehicles acting as flexible demands responding to power signals to minimize the system peaks. The proposed hierarchical autonomous demand side management algorithm is formulated as an optimal power tracking problem. The distribution grid operator determines a power signal for filling the valleys in the non-electric vehicle load profile using the electric vehicle demand flexibility and sends it to all electric vehicle controllers. After receiving the control signal, each electric vehicle controller re-scales it to the expected individual electric vehicle energy demand and determines the optimal charging schedule to track the re-scaled signal. No information concerning the electric vehicles are reported back to the utility, hence the approach can be implemented using unidirectional communication with reduced infrastructural requirements. The achieved results show that the optimal power tracking approach has the potential to eliminate additional peak demands induced by electric vehicle charging and performs comparably to its central implementation. The reduced complexity and computational overhead permits also convenient deployment in practice.
Industrial demand side management has shown significant potential to increase the efficiency of industrial energy systems via flexibility management by model-driven optimization methods. We propose a grey-box model of an industrial food processing plant. The model relies on physical and process knowledge and mass and energy balances. The model parameters are estimated using a predictive error method. Optimization methods are applied to separately reduce the total energy consumption, total energy costs and the peak electricity demand of the plant. A viable potential for demand side management in the plant is identified by increasing the energy efficiency, shifting cooling power to low price periods or by peak load reduction.