Akin Tascikaraoglu

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.

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.

Low-dimensional models in spatio-temporal wind speed forecasting

Integrating wind power into the grid is challenging because of its random nature. Integration is facilitated with accurate short-term forecasts of wind power. The paper presents a spatio-temporal wind speed forecasting algorithm that incorporates the time series data of a target station and data of surrounding stations. Inspired by Compressive Sensing (CS) and structured-sparse recovery algorithms, we claim that there usually exists an intrinsic low-dimensional structure governing a large collection of stations that should be exploited. We cast the forecasting problem as recovery of a block-sparse signal x from a set of linear equations b = Ax for which we propose novel structure-sparse recovery algorithms. Results of a case study in the east coast show that the proposed Compressive Spatio-Temporal Wind Speed Forecasting (CSTWSF) algorithm significantly improves the short-term forecasts compared to a set of widely-used benchmark models.

An energy credit based incentive mechanism for the direct load control of residential HVAC systems incorporation in day-ahead planning

The increasing operational complexity of power systems considering the higher renewable energy penetration and changing load characteristics, together with the recent developments in the ICT field have led to more research and implementation efforts related to the activation of the demand side. In this manner, different direct load control (DLC) and indirect load control concepts have been developed and DLC strategies are considered as an effective tool for load serving entities (LSEs) with several real-world application examples. In this study, a new DLC strategy tailored for residential air-conditioners (ACs) participating in the day-ahead planning, based on offering energy credits to the enrolled end-users is proposed. The mentioned energy credits are then used by residential end-users to lower their energy procurement costs during peak-price periods. The strategy is formulated as a stochastic mixed-integer linear programming (MILP) model considering uncertainties related to weather conditions. The outcomes regarding the end-user comfort level and economic benefits are also analyzed.

A short-term spatio-temporal approach for Photovoltaic power forecasting

This paper presents a Photovoltaic (PV) power conversion model and a forecasting approach which uses spatial dependency of variables along with their temporal information. The power produced by a PV plant is forecasted by a PV conversion model using the predictions of three weather variables, namely, irradiance on the tilted plane, ambient temperature, and wind speed. The predictions are accomplished using a spatio-temporal algorithm that exploits the sparsity of correlations between time series data of different meteorological stations in the same region. The performances of the forecasting algorithm as well as the PV conversion model are investigated using real data recorded at various locations in Italy. The comparisons with various benchmark methods show the effectiveness of the proposed approaches over short-term forecasts.

A peak power reduction based demand response strategy and load factor maximization oriented electric vehicle parking lot energy management strategy

Ulaşım sistemlerinin elektrifikasyonu üzerine son zamanlarda artan ilgi ile birlikte elektrikli araçlar üzerine gerçekleştirilen çalışmalar büyük ivme kazanmıştır. Ancak elektrikli araçlar dağıtım seviyesinden elektrik güç sistemine bağlandıklarından dolayı artan elektrikli araç şarj gereksinimi nedeniyle sistemde önemli bir güç talebi artışı oluşacaktır. Bireysel olarak elektrikli araçların dağıtım sistemine asgari yükü getirecek şekilde koordine edilmesi oldukça zor olsa da özellikle elektrikli araç toplu park bölgeleri bünyesinde ilgili şarj işleminin yönetimi etkin bir opsiyondur. Bu durum özellikle son zamanlarda akıllı şebekeler kapsamındaki talep cevabı konsepti ile de ilişkilendirilmektedir. Bu bağlamda bu çalışmada, pik güç azaltımı tabanlı bir talep cevabı stratejisinin gereksinimini karşılayacak ve aynı zamanda ilgili şarj gücü değişiminin yük faktörünü azami hale getirecek şekilde bir işletim sağlayacak bir enerji yönetim stratejisi önerilmektedir. Together with the increasing attention on the electrification of transportation systems, the studies realized on electric vehicles have gained a great acceleration. However, as the electric vehicles are connected to the electric power system from the distribution level, an important power demand increase will occur in the system due to the electric vehicle charging requirements. Even the coordination of individual electric vehicles so as to bring minimum loading to the distribution system is significantly hard, especially the management of the relevant charging process within the electric vehicle parking lots is an effective option. Specifically, this issue has been linked with the demand response concept in smart grid content. In this regard, in this study an energy management strategy that can ensure the requirements of a peak power reduction oriented demand response strategy and can provide an operation that maximizes the load factor of the relevant charging power variation is proposed.

On Data-Driven Approaches for Demand Response

Chapter Overview This chapter presents a detailed investigation on the resources, use, and benefits of big data analytics in smart grid activities that enable the participation of demand side in energy management. It starts by elucidating these activities called demand-side management and demand response (DR) and their role in providing higher saving potential for both system operators and end users. It then explains the use of big data management techniques in order to handle the huge amount of data required for efficient DR applications. Afterward, the benefit of various clustering methods and classification methods in DR applications is evaluated by classifying them into four main groups according to their objectives. First, the role of big data analytics on the energy consumption behavior of end users and on the electric load classification is examined. Then, the support of DR programs relying on big data analytics is evaluated for demand and renewable energy generation forecasting as well as dynamic pricing.

Comprehensive Optimization Model for Sizing and Siting of DG Units, EV Charging Stations, and Energy Storage Systems

The sizing and siting of renewable resources-based distributed generation (DG) units has been a topic of growing interest, especially during the last decade due to the increasing interest in renewable energy systems and the possible impacts of their volatility on distribution system operation. This paper goes beyond the existing literature by presenting a comprehensive optimization model for the sizing and siting of different renewable resources-based DG units, electric vehicle charging stations, and energy storage systems within the distribution system. The proposed optimization model is formulated as a second order conic programming problem, considering also the time-varying nature of DG generation and load consumption, in contrast with the majority of the relevant studies that have been based on static values.

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.