Priyanka Shinde

Bio: Priyanka Shinde is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: Electricity market & Market liquidity. The author has an hindex of 4, co-authored 10 publications receiving 63 citations. Previous affiliations of Priyanka Shinde include Indian Institute of Technology Madras & Indian Institute of Technology Delhi.

A Literature Review of Intraday Electricity Markets and Prices

Abstract: Renewable energy sources are inherently stochastic in nature and as a result might lead to high balancing costs. Allowing trade closer to the time of delivery is a potential solution. Intraday markets provide this possibility to the market participants to adjust their positions in the market based on the updated forecast. However, the way in which intraday markets are set-up, they face several challenges like low liquidity, high price volatility leading to low trading activities. In this paper, we provide an overview of the literature on intraday electricity markets and prices. Several studies have been highlighted which focus on the bidding strategies for different types of producers and consumers. Some literature on specific electricity markets is also discussed in the paper. There exists some research work on intraday electricity prices, which has also been summarized. Research gaps and possibility of future work in each of these topics have been discussed.

Stackelberg game-based demand response in multiple utility environments for electric vehicle charging

Abstract: An upsurge in the electricity demand seems inevitable due to the large-scale deployment of electric vehicles (EVs). Demand response, which is a potential avenue to curb this demand, aims at reduction of power generation costs and electricity bills by allowing control of electricity consumption through electricity prices. This study proposes a holistic approach to combining the behaviour of EV users and customers with other elastic loads participating in demand response to make the scenario more realistic. In this study, various cases with single and multiple utility companies (UCs), which try to set the prices in such a way so as to maximise their profits, have been considered. A Stackelberg game model has been designed to address this conflict of interests between the UCs and the customers. This study considers different utility functions for different types of customers in order to meet their energy requirements meanwhile maximising the profits of the UCs at the Stackelberg equilibrium. The impact of the increase in competition is also studied.

Nordic Balancing Markets: Overview of Market Rules

Abstract: System operators have the option to trade balancing reserves among countries and operators. In order to trade balancing reserves with other system operators the markets should be harmonized. While the spot and intraday markets are already harmonized within the Nordics, the balancing markets still display differences. The differences can be subtle, yet they may play a significant role for the planning, operation, modelling and control of the power system. In this paper, we conduct a thorough literature review on Nordic balancing markets and summarize the market rules and requirements. This review can help operators and modellers to better represent the Nordic power system.

Optimal Dispatch in a Balancing Market With Intermittent Renewable Generation

Abstract: In this paper, three single-stage stochastic programs are proposed and compared for optimal dispatch by a System Operator (SO) into balancing markets (BM).The motivation for the models is to represent a possible requirement to undertake system balancing with increasing amounts of intermittent renewable generation. The proposed optimization models are reformulated as tractable Mixed Integer Linear Programs (MILPs) and these consider both fuel cost and intermittency cost of the generators, when the SO activates the up- or down-regulation bids. These three models are based on the main approaches seen in practice: dual-imbalance pricing, single imbalance pricing and single imbalance pricing with spot reversion. A scenario-generation algorithm based on predictive conditional dynamic density distributions is also proposed. We perform a comparative analysis of these three proposed models in terms of how they help the SO to optimize their balancing market actions considering intermittent-renewable generators. The single imbalance pricing is found to be the most market efficient.

Optimal Electric Vehicle charging schedule for demand side management

Abstract: Demand side management (DSM) aims at reduction of the peak demand thereby flattening the load profile by allowing customers to actively participate in the overall operation of the smart grid. This leads to significant reduction in total generation cost and emission levels. While introduction of Electric Vehicles (EV) in the grid can be looked upon as an additional load thus arising the need for increased generation, the price elasticity of EV load can serve as an opportunity to implement DSM. A V2G strategy is proposed to enable the reshaping of load profile by means of peak shaving and valley filling. By harnessing the energy storage of EVs batteries, an objective function is proposed for V2G control. Substantial savings and reduction in emissions along with reduction in peak demand, on implementation of this strategy, is evident from the simulation results. Optimal penetration level of EVs, based on the environmental consideration is also discussed.

Electric Vehicle into the Grid: Charging Methodologies Aimed at Providing Ancillary Services Considering Battery Degradation

Abstract: The necessity of transport electrification is already undeniable due to, among other facts, global Greenhouse Gas (GHG) emissions and fossil-fuel dependency. In this context, electric vehicles (EVs) play a fundamental role. Such vehicles are usually seen by the network as simple loads whose needs have to be supplied. However, they can contribute to the correct operation of the network or a microgrid and the provision of ancillary services and delay the need to reinforce the power lines. These concepts are referred to as Vehicle-to-Grid (V2G), Vehicle-to-Building (V2B) and Vehicle-to-Home (V2H). In paper, a deep classification and analysis of published charging strategies is provided. In addition, optimal charging strategies must minimise the degradation of the batteries to increase their lifetime, since it is considered that the life of a battery ends when its capacity is reduced by 20% with respect to its nominal capacity. Therefore, an optimal integration of EVs must consider both grid and batteries impact. Finally, some guidelines are proposed for further research considering the current limitations of electric vehicle technology. Thus, these proposed guidelines are focused on V2G optimal management, enabling new business models while keeping economic viability for all parts involved.

Minimization of Load Variance in Power Grids—Investigation on Optimal Vehicle-to-Grid Scheduling

Abstract: The introduction of electric vehicles into the transportation sector helps reduce global warming and carbon emissions. The interaction between electric vehicles and the power grid has spurred the emergence of a smart grid technology, denoted as vehicle-to grid-technology. Vehicle-to-grid technology manages the energy exchange between a large fleet of electric vehicles and the power grid to accomplish shared advantages for the vehicle owners and the power utility. This paper presents an optimal scheduling of vehicle-to-grid using the genetic algorithm to minimize the power grid load variance. This is achieved by allowing electric vehicles charging (grid-to-vehicle) whenever the actual power grid loading is lower than the target loading, while conducting electric vehicle discharging (vehicle-to-grid) whenever the actual power grid loading is higher than the target loading. The vehicle-to-grid optimization algorithm is implemented and tested in MATLAB software (R2013a, MathWorks, Natick, MA, USA). The performance of the optimization algorithm depends heavily on the setting of the target load, power grid load and capability of the grid-connected electric vehicles. Hence, the performance of the proposed algorithm under various target load and electric vehicles’ state of charge selections were analysed. The effectiveness of the vehicle-to-grid scheduling to implement the appropriate peak load shaving and load levelling services for the grid load variance minimization is verified under various simulation investigations. This research proposal also recommends an appropriate setting for the power utility in terms of the selection of the target load based on the electric vehicle historical data.

Aggregator’s business models in residential and service sectors: A review of operational and financial aspects

Abstract: Flexibility coming from consumers in residential and service sectors has received significant attention to deal with uncertainty and variability of renewable energy sources. Since these consumers are too small individually to participate in the electricity markets, their assets can be pooled by an aggregator. The aggregator can implement business models by trading flexibility obtained from these consumers’ assets in different electricity markets. However, the aggregator and the consumers are only motivated to implement a business model, if it is economically feasible. The economic feasibility of a business model depends on (1) financial aspects: how much profit the aggregator makes, and how much money the consumers save, and (2) operational aspects: how the consumers’ assets are operated to increase the financial aspects. This paper aims to provide insights in these operational and financial aspects of the aggregator's business models in residential and service sectors. For this purpose, a literature review is conducted, and a framework is presented to analyze the selected papers on these operational and financial aspects. Based on this analysis, different strategies for the aggregator to implement business models are determined. Moreover, knowledge gaps are identified and several recommendations for future research are provided.

Performance Analysis of Cooperative V2V and V2I Communications Under Correlated Fading

Abstract: Cooperative vehicular networks will play a vital role in the coming years to implement various intelligent transportation related applications. Both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications will be needed to reliably disseminate information in a vehicular network. In this regard, a roadside unit (RSU) equipped with multiple antennas can improve the network capacity. While the traditional approaches assume antennas to experience independent fading, we consider a more practical uplink scenario where antennas at the RSU experience correlated fading. In particular, we evaluate the packet error probability for two renowned antenna correlation models, i.e., constant correlation (CC) and exponential correlation (EC). We also consider intermediate cooperative vehicles for reliable communication between the source vehicle and the RSU. Here, we derive closed-form expressions for packet error probability, which help to quantify the performance variations due to fading parameter, correlation coefficients, and the number of intermediate helper vehicles. To evaluate the optimal transmit power in this network scenario, we formulate a Stackelberg game, wherein, the source vehicle is treated as a buyer and the helper vehicles are the sellers. The optimal solutions for the asking price and the transmit power are devised which maximize the utility functions of helper vehicles and the source vehicle, respectively. We verify our mathematical derivations by extensive simulations in MATLAB.

Neural Network Based Model Comparison for Intraday Electricity Price Forecasting

Abstract: The intraday electricity markets are continuous trade platforms for each hour of the day and have specific characteristics. These markets have shown an increasing number of transactions due to the requirement of close to delivery electricity trade. Recently, intraday electricity price market research has seen a rapid increase in a number of works for price prediction. However, most of these works focus on the features and descriptive statistics of the intraday electricity markets and overlook the comparison of different available models. In this paper, we compare a variety of methods including neural networks to predict intraday electricity market prices in Turkish intraday market. The recurrent neural networks methods outperform the classical methods. Furthermore, gated recurrent unit network architecture achieves the best results with a mean absolute error of 0.978 and a root mean square error of 1.302. Moreover, our results indicate that day-ahead market price of the corresponding hour is a key feature for intraday price forecasting and estimating spread values with day-ahead prices proves to be a more efficient method for prediction.