Nikolaos G. Paterakis

Smart Household Operation Considering Bi-Directional EV and ESS Utilization by Real-Time Pricing-Based DR

As the smart grid solutions enable active consumer participation, demand response (DR) strategies have drawn much interest in the literature recently, especially for residential areas. As a new type of consumer load in the electric power system, electric vehicles (EVs) also provide different opportunities, including the capability of utilizing EVs as a storage unit via vehicle-to-home (V2H) and vehicle-to-grid (V2G) options instead of peak power procurement from the grid. In this paper, as the main contribution to the literature, a collaborative evaluation of dynamic-pricing and peak power limiting-based DR strategies with a bi-directional utilization possibility for EV and energy storage system (ESS) is realized. A mixed-integer linear programming (MILP) framework-based modeling of a home energy management (HEM) structure is provided for this purpose. A distributed small-scale renewable energy generation system, the V2H and V2G capabilities of an EV together with two-way energy trading of ESS, and different DR strategies are all combined in a single HEM system for the first time in the literature. The impacts of different EV owner consumer preferences together with the availability of ESS and two-way energy trading capabilities on the reduction of total electricity prices are examined with case studies.

Optimal Household Appliances Scheduling Under Day-Ahead Pricing and Load-Shaping Demand Response Strategies

In this paper, a detailed home energy management system structure is developed to determine the optimal day-ahead appliance scheduling of a smart household under hourly pricing and peak power-limiting (hard and soft power limitation)-based demand response strategies. All types of controllable assets have been explicitly modeled, including thermostatically controllable (air conditioners and water heaters) and nonthermostatically controllable (washing machines and dishwashers) appliances, together with electric vehicles (EVs). Furthermore, an energy storage system (ESS) and distributed generation at the end-user premises are taken into account. Bidirectional energy flow is also considered through advanced options for EV and ESS operation. Finally, a realistic test-case is presented with a sufficiently reduced time granularity being thoroughly discussed to investigate the effectiveness of the model. Stringent simulation results are provided using data gathered from real appliances and real measurements.

Coordinated Operation of a Neighborhood of Smart Households Comprising Electric Vehicles, Energy Storage and Distributed Generation

In this paper, the optimal operation of a neighborhood of smart households in terms of minimizing the total energy procurement cost is analyzed. Each household may comprise several assets such as electric vehicles, controllable appliances, energy storage and distributed generation. Bi-directional power flow is considered both at household and neighborhood level. Apart from the distributed generation unit, technological options such as vehicle-to-home and vehicle-to-grid are available to provide energy to cover self-consumption needs and to inject excessive energy back to the grid, respectively. The energy transactions are priced based on the net-metering principles considering a dynamic pricing tariff scheme. Furthermore, in order to prevent power peaks that could be harmful for the transformer, a limit is imposed to the total power that may be drawn by the households. Finally, in order to resolve potential competitive behavior, especially during relatively low price periods, a simple strategy in order to promote the fair usage of distribution transformer capacity is proposed.

End-User Comfort Oriented Day-Ahead Planning for Responsive Residential HVAC Demand Aggregation Considering Weather Forecasts

There is a remarkable potential for implementing demand response (DR) strategies for several purposes such as peak load reduction, frequency regulation, etc. by using thermostatically-controllable appliances (TCAs). In this study, an end-user comfort violation minimization oriented DR strategy for residential heating, ventilation and air conditioning (HVAC) units is proposed. The proposed approach manipulates the temperature set-point of HVAC thermostats aiming to minimize the average discomfort among end-users enrolled in a DR program, while satisfying the DR event related requirements of the load serving entity. Besides, the fairness for the allocation of the comfort violation among enrolled end-users is also taken into account. Moreover, maintaining the load factor during the contracted DR period compared to a base case in order to reduce the load rebound effect due to shifting the use of HVAC units is also provided with the proposed strategy. Last but not least, the heat index considering the impact of humidity is utilized instead of using ambient dry-bulb temperature through a spatiotemporal forecasting approach.

Load-Following Reserves Procurement Considering Flexible Demand-Side Resources Under High Wind Power Penetration

Summary form only given. The variable and uncertain nature of the leading renewable energy resources, such as wind power generation, imposes the development of a sophisticated balance mechanism between supply and demand to maintain the consistency of a power system. In this study, a two stage stochastic programming model is proposed to procure the required load-following reserves from both generation and demand side resources under high wind power penetration. Besides, a novel load model is introduced to procure flexible reserves from industrial clients. Load following reserves from Load Serving Entities (LSE) are also taken into account as well as network constraints, load shedding and wind spillage. The proposed methodology is applied to an illustrative test system, as well as to a 24-node system.

Multi-Objective Reconfiguration of Radial Distribution Systems Using Reliability Indices

This paper deals with the distribution network reconfiguration problem in a multi-objective scope, aiming to determine the optimal radial configuration by means of minimizing the active power losses and a set of commonly used reliability indices formulated with reference to the number of customers. The indices are developed in a way consistent with a mixed-integer linear programming (MILP) approach. A key contribution of the paper is the efficient implementation of the å-constraint method using lexicographic optimization in order to solve the multi-objective optimization problem. After the Pareto efficient solution set is generated, the resulting configurations are evaluated using a backward/forward sweep load-flow algorithm to verify that the solutions obtained are both non-dominated and feasible. Since the å-constraint method generates the Pareto front but does not incorporate decision maker (DM) preferences, a multi-attribute decision making procedure, namely, the technique for order preference by similarity to ideal solution (TOPSIS) method is used in order to rank the obtained solutions according to the DM preferences, facilitating the final selection. The applicability of the proposed method is assessed on a classical test system and on a practical distribution system.

Assessment of Demand-Response-Driven Load Pattern Elasticity Using a Combined Approach for Smart Households

The recent interest in the smart grid vision and the technological advancement in the communication and control infrastructure enable several smart applications at different levels of the power grid structure, while specific importance is given to the demand side. As a result, changes in load patterns due to demand response (DR) activities at end-user premises, such as smart households, constitute a vital point to take into account both in system planning and operation phases. In this study, the impact of price-based DR strategies on smart household load pattern variations is assessed. The household load datasets are acquired using model of a smart household performing optimal appliance scheduling considering an hourly varying price tariff scheme. Then, an approach based on artificial neural networks (ANN) and wavelet transform (WT) is employed for the forecasting of the response of residential loads to different price signals. From the literature perspective, the contribution of this study is the consideration of the DR effect on load pattern forecasting, being a useful tool for market participants such as aggregators in pool-based market structures, or for load serving entities to investigate potential change requirements in existing DR strategies, and effectively plan new ones.

Compressive Spatio-Temporal Forecasting of Meteorological Quantities and Photovoltaic Power

This paper presents a solar power forecasting scheme, which uses spatial and temporal time series data along with a photovoltaic (PV) power conversion model. The PV conversion model uses the forecast of three different variables, namely, irradiance on the tilted plane, ambient temperature, and wind speed, in order to estimate the power produced by a PV plant at the grid connection terminals. The forecast values are obtained using a spatio-temporal method that uses the data recorded from a target meteorological station as well as data of its surrounding stations. The proposed forecasting method exploits the sparsity of correlations between time series data in a collection of stations. The performance of both the PV conversion model and the spatio-temporal algorithm is evaluated using high-resolution real data recorded in various locations in Italy. Comparison with other benchmark methods illustrates that the proposed method significantly improves the solar power forecasts, particularly over short-term horizons.

Qualification and Quantification of Reserves in Power Systems Under High Wind Generation Penetration Considering Demand Response

The presence of high levels of renewable energy resources (RES) and especially wind power production poses technical and economic challenges to system operators, which under this fact have to procure more ancillary services (AS) through various balancing mechanisms, in order to maintain the generation-consumption balance and to guarantee the security of the grid. Traditionally, these critical services had been procured only from the generation side, yet the current perception has begun to recognize the demand side as an important asset that can improve the reliability of a power system, offering notable advantages. In this study, a two-stage stochastic programming model, representing the day-ahead market clearing procedure on an hourly basis and the actual minute-to-minute operation of the power system, is developed comprising different services that specifically address various disturbance sources of the normal operation of a power system, namely intra-hour load variation, intra-hour wind variation, as well as generating unit and transmission line outages.

Consideration of the Impacts of a Smart Neighborhood Load on Transformer Aging

Smart grid solutions with enabling technologies such as energy management systems (EMSs) and smart meters promote the vision of smart households, which also allows for active demand side in the residential sector. These technologies enable the control of residential consumption, local small-scale generation, and energy storage systems to respond to time-varying prices. However, shifting loads simultaneously to lower price periods is likely to put extra stress on distribution system assets such as distribution transformers. Especially, additional new types of loads/appliances such as electric vehicles (EVs) can introduce even more burden on the operation of these assets, which is an issue that needs special attention. Such extra stress can cause accelerated aging of distribution system assets and significantly affect the reliability of the system. In this paper, the impact of a smart neighborhood load on distribution transformer aging is investigated. The EMS of each household is designed to respond to prices and other signals emitted by the responsive load serving entity within the relevant demand response strategy. An optimization framework based on mixed-integer linear programming is presented in order to define the EMS structure. Then, the equivalent aging of the distribution transformer is examined with a thermal model under different scenarios. The case studies that are presented indicate that the integration of EVs in residential premises may indeed cause accelerated aging of the distribution transformers, while the need to investigate the efficiency of dynamic pricing mechanisms is rendered evident.