A villamosenergia-fogyasztás elhalasztásával kapcsolatos lakossági attitűd felmérése Magyarországon

Az Európai Unió új energiapolitikai csomagjában fontos szerepet kaptak a keresletoldali szabályozást koordináló megoldások. A dokumentum a nemzetállamokat is ösztönzi a lakosság aktív részvételének előmozdítására. A tanulmány azt vizsgálja, hogy mely háztartási eszközök esetében mely társadalmi csoportok vonhatók be keresletoldali rugalmassági akciókba. Az elemzéshez készített közvélemény-kutatás véletlenszerűen kiválasztott 1001 fő válaszait tartalmazza, ez a minta a magyar háztartásokra nézve a településtípus, a háztartásfő életkora és iskolai végzettsége szempontjából reprezentatív. A kutatáshoz – a leíró alapstatisztika mellett – a Kruskal–Wallis-próbát használtuk a különböző csoportok és változók elemzésére. Öt napszakot vizsgáltunk: reggel, délelőtt, délután, este és éjszaka. A kvantitatív kutatás során szignifikáns eltérést találtunk a kereslet potenciális átütemezésével kapcsolatos fogyasztói attitűd és a villamosenergia-­eszközeik használati időpontja között. Ezenkívül megállapítható, hogy szignifikáns eltérések mutatkoznak a különböző demográfiai csoportok között a kereslet rugalmas ütemezésének attitűdjében.* Journal of Economic Literature (JEL) kód: Q41, Q48

Optimal scheduling for charging electric vehicles with fixed setup costs.

The increasing popularity of electric vehicles (EV) will pose great challenge to the nation\u27s existing power grid by adding extra load during evening peak hours. This thesis develops a centralized optimal charging scheduling (OCS) model with a mixed integer nonlinear program to mitigate the negative impact of extra load from EVs on the power grid. The objective of the OCS model is to minimize the energy cost of the entire system and fixed setup costs for day-time charging, which essentially levels the load of the entire power grid throughout a day under the dynamic pricing environment. Furthermore, a rolling horizon heuristic algorithm is proposed as an alternative solution that addresses large scale OCS instances. Finally, when centralized scheduling is impractical, this thesis proposes a decentralized optimal charging heuristic using the concepts of game theory and coordinate search. Numerical results show that the optimal charging scheduling model can significantly lower the total energy cost and the peak-to-average ratio (PAR) for a power system. When compared to uncontrolled charging, the decentralized charging heuristic yields considerable energy savings as well, although not as efficient as the centralized optimal charging solutions

Steps Toward a Net-Zero Campus with Renewable Energy Resources

With the increasing attention and support behind plug in hybrid electric vehicles, research must be conducted to examine the impacts of vehicles on electric distribution and transmission systems. This research aims first to model the behavior of vehicle battery chargers during system disturbances and mitigate any impacts. A distribution test system example is modeled and several different vehicle charger topologies are added. Faults are applied to the distribution system with vehicle chargers connected and the results are examined. Based on these results, a control strategy to mitigate their negative impacts is suggested. Photovoltaic panels are then added to the system and the study is repeated. Several services that plug in hybrid electric vehicles are capable of providing to the electric system are presented in order to allow electric vehicles to be seen as an asset to electric systems rather than a burden. These services are particularly focused on an electric system such as might be found on a college campus, which in this case is represented by the Clemson University electric distribution system. The first service presented is dynamic phase balancing of a distribution system using vehicle charging. Distribution systems typically face problems with unbalance. At most large car parks, a three phase electric supply is expected even though current standardized chargers are single phase. By monitoring system unbalance and choosing which phase a vehicle is allowed to charge from, unbalance between phases is reduced in a distribution system. The second service presented is a decentralized vehicle to campus control algorithm based on time of use rates. Using time of use electricity prices, discharging vehicle batteries during high prices and recharging at low prices is explored. Battery degradation as well as limits placed by required vehicle range availability are included in the decision on whether to charge or discharge. Electric utilities will also benefit from a reduction of load at peak times if vehicles discharge back to the campus. A comparison with stationary battery energy storage is included

Considerations of the Impacts and System-level Mitigation of Electric Vehicle Charging on the Integrated Resource Planning Process

Electric vehicles (EV) are growing in popularity and therefore adoption rate. Best estimates predict a 6.2% EV adoption rate by 2035 in the southeastern United States. With this level of EV adoption, utility planners must begin to consider the impact that EVs will have on the power grid. This paper aims to help predict these EV impacts on the power grid. Specifically, an urban-commercial feeder is analyzed in detail to provide worst-case and most-likely results of varying levels of EV impact. Results show that a 26.2% peak increase is the most likely result for this feeder in 2035. Mitigation techniques are used to lower the impact that EVs will have on the power grid. A number of mitigation techniques are specifically analyzed for this urban-commercial feeder. The cost of each of these mitigation techniques is compared to their effectiveness. The best mitigation strategy is chosen to be a combination of time-of-use and a battery energy storage system because it gives the best results relative to cost and provides emergency capabilities. In this study, system data is extracted from this urban-commercial feeder and combined with other feeder types to provide a utility scale EV impact. The scale-up model provides the most-likely scenario for future EV impacts on the entire utility’s power grid. This system level scale-up data can be used for integrated resource planning purposes

Optimization models and algorithms for demand response in smart grid.

For demand response in smart grid, a utility company wants to minimize total electricity cost and end users want to maximize their own utility. The latter is considered to consist of two parts in this research: electricity cost and convenience/comfort. We first develop a system optimal (SO) model and a user equilibrium (UE) model for the utility company and end users, respectively and compare the difference of the two. We consider users\u27 possible preference on convenience over cost-saving under the real-time pricing in smart grid, and each user is assumed to have a preferred time window for using a particular appliance. As a result, each user in the proposed energy consumption game wishes to maximize a payoff or utility consisting of two parts: the negative of electricity cost and the convenience of using appliances during their preferred time windows. Numerical results show that users with less flexibility on their preferred usage times have larger impact on the system performance at equilibrium. Second, we found that instead of minimizing total cost, if utility company is regulated to maximize the social welfare, the user equilibrium model can achieve identical optimal solution as the system optimal model. We then design a demand response pricing frame work to accomplish this goal under alternative secondary objectives. We also investigate the non-uniqueness of the user equilibrium solution and prove that there exist alternative user equilibrium solutions. In this case, robust pricing is considered using multi-level optimization for the user equilibrium. Third, we study empirical data from a demand response pilot program in Kentucky in an attempt to understand consumer behavior under demand response and to characterize the thermo dynamics when set point for heat, ventilation and air conditioning (HVAC) is adjusted for demand response. Although sample size is limited, it helps to reveal the great variability in consumers\u27 response to demand response event. Using the real data collected, we consider to minimize the peak demand for a system consisting of smart thermostats, advanced hot water heaters and battery systems for storage. We propose a mixed integer program model as well as a heuristic algorithm for an optimal consumption schedule so that the system peak during a designated period is minimized. Therefore, we propose a consumption scheduling model to optimally control these loads and storage in maximizing efficiency without impacting thermal comfort. The model allows pre-cooling and pre-heating of homes to be performed for variable loads in low-demand times. We propose several future works. First, we introduce the concept of elastic demand to our SO model and UE model. The system problem maximizes net benefit to the energy consumers and the user problem is the usual one of finding equilibrium with elastic demand. The Karush-Kuhn-Tucker (KKT) conditions can be applied to solve the elastic demand problems. We also propose to develop algorithms for multi-level pricing models and further collect and analyze more field data in order to better understand energy users\u27 consumption behavior

Investigation of energy storage system and demand side response for distribution networks

PhD ThesisThe UK government has a target of achieving an 80% reduction in CO2 emissions with respect to the values from 1990 by 2050. Therefore, renewables based distributed generations (DGs) coupled with substantial electrification of the transport and heat sectors though low carbon technologies (LCTs), will be essential to achieve this target. The anticipated proliferation of these technologies will necessitate major opportunities and challenges to the operation and planning of future distribution networks. Smartgrid technologies and techniques, such as energy storage systems (ESSs), demand side response (DSR) and real time thermal ratings (RTTRs), provide flexible, economic and expandable solutions to these challenges without resorting to network reinforcement. This research investigates the use of ESS and DSR in future distribution networks to facilitate LCTs with a focus on the management and resolution of thermal constraints and steady state voltage limit violation problems. Firstly, two control schemes based on sensitivity factors and cost sensitivity factors are proposed. Next, the impacts of a range of sources of uncertainties, arising from existing and future elements of the electrical energy system, are studied. The impacts of electric vehicle charging are investigated with Monte Carlo simulation (MCS). Furthermore, to deal with uncertainties efficiently, a scheduling scheme based on robust optimization (RO) is developed. Two approaches have been introduced to estimate the trade-off between the cost and the probability of constraint violations. Finally, the performance of this scheme is evaluated. The results of this research show the importance of dealing with uncertainties appropriately. Simulation results demonstrate the capability and effectiveness of the proposed RO based scheduling scheme to facilitate DG and LCTs, in the presence of a range of source of uncertainties. The findings from this research provide valuable solution and guidance to facilitate DG and LCTs using ESS, DSR and RTTR in future distribution networks