Stylianos I. Vagropoulos

Optimal Bidding Strategy for Electric Vehicle Aggregators in Electricity Markets

This paper determines the optimal bidding strategy of an electric vehicle (EV) aggregator participating in day-ahead energy and regulation markets using stochastic optimization. Key sources of uncertainty affecting the bidding strategy are identified and incorporated in the stochastic optimization model. The aggregator portfolio optimization model should include inevitable deviations between day-ahead cleared bids and actual real-time energy purchases as well as uncertainty for the energy content of regulation signals in order to ensure profit maximization and reliable reserve provision. Energy deviations are characterized as “uninstructed” or “instructed” depending on whether or not the responsibility resides with the aggregator. Price deviations and statistical characteristics of regulation signals are also investigated. Finally, a new battery model is proposed for better approximation of the battery charging characteristic. Test results with an EV aggregator representing one thousand EVs are presented and discussed.

Application of time series and artificial neural network models in short-term forecasting of PV power generation

This paper addresses two practical methods for electricity generation forecasting of grid-connected PV plants. The first model is based on seasonal ARIMA time-series analysis and is further improved by incorporating short-term solar radiation forecasts derived from NWP models. The second model adopts artificial neural networks with multiple inputs. Day-ahead and rolling intra-day forecast updates are implemented to evaluate the forecasting errors. All models are compared in terms of the Normalized (with respect to the PV installed capacity) Root Mean Square Error (NRMSE). Simulation results from the application of the forecasting models in different PV plants of the Greek power system are presented.

Real-Time Charging Management Framework for Electric Vehicle Aggregators in a Market Environment

A framework for real-time (RT) charging management of an electric vehicle aggregator (EVA) participating in electric energy and regulation markets is proposed. The developed models, which assign charging set points to the electric vehicles (EVs) based on evolving EV charging priorities, are formulated as linear programs that can be solved very fast. A model of the most common (constant current-constant voltage) battery charging method is also presented. A case study is examined, where an EVA representing 1000 EVs participates in the day-ahead and RT energy and regulation markets, and manages the RT charging of the EVs in his fleet based on the developed framework. Through this approach, conclusions on the impact of the charging priority parameterization for the RT EV charging management, the value of the dynamic regulation signal on the reliable EV participation in the regulation market, and the impact of the detailed battery modelling are derived.

An Investigation of Plug-In Electric Vehicle Charging Impact on Power Systems Scheduling and Energy Costs

This paper investigates the impact of plug-in electric vehicle (EV) integration on the power systems scheduling and energy cost. An intermediary entity, the EV aggregator, participates in the market on behalf of the EV owners by optimally self-scheduling under the price-taking approach. Through detailed rolling simulations for a year and different EVs’ penetration scenarios at a large insular power system, this work highlights the different impact of direct and smart charging on power system scheduling and energy costs, the limitations of the price-taking approach, which is widely used in self-scheduling models, and the difference in system value and market value that smart charging adoption creates in restructured markets under the marginal pricing rule.

Synergistic supply offer and demand bidding strategies for wind producers and electric vehicle aggregators in day-ahead electricity markets

This work evaluates the opportunities for increased profits owing to the better management of energy deviations under a synergistic supply offer and demand bidding strategy of a wind energy producer and an electric vehicle (EV) aggregator that participate in day-ahead energy and regulation reserve market. The new market player acts as a prosumer and participates in the electricity market with synergistic offers and bids of the two entities he represents. Key factors of uncertainty affecting the bidding strategy are identified and incorporated in a stochastic optimization framework. The case of night residential EV charging is examined and unidirectional interaction between EVs and the grid is considered, i.e. the EVs do not discharge energy back to the grid. The possibility for increased profits through the holistic consideration and better management of the energy deviations under specific market rules is examined in this work. Finally, the impact of wind curtailment opportunity in energy deviation management was also examined.

Comparison of SARIMAX, SARIMA, modified SARIMA and ANN-based models for short-term PV generation forecasting

This paper compares four practical methods for electricity generation forecasting of grid-connected Photovoltaic (PV) plants, namely Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling, SARIMAX modeling (SARIMA modeling with exogenous factor), modified SARIMA modeling, as a result of an a posteriori modification of the SARIMA model, and ANN-based modeling. Interesting results regarding the necessity and the advantages of using exogenous factors in a time series model are concluded from this comparison. Finally, intra-day forecasts updates are implemented to evaluate the forecasting errors of the SARIMA and the SARIMAX models. Their comparison highlights differences in accuracy between the two models. All models are compared in terms of the Normalized (with respect to the PV installed capacity) Root Mean Square Error (NRMSE) criterion. Simulation results from the application of the forecasting models in a PV plant in Greece using real-world data are presented.

Financial viability of investments on electric vehicle charging stations in workplaces with parking lots under flat rate retail tariff schemes

This paper assesses the financial viability of an investment on Electric Vehicle Charging Stations (EVCSs) from a commercial/industrial workplace with a parking lot, which acts as a reseller that purchases energy from the grid and then sells energy to the Electric Vehicle (EV) owners at a new flat rate tariff. Investment cost drivers (expected EVCSs costs, anticipated EV charging needs, EVCSs operational life etc.) are specified by associated market researches. Two typical capital budgeting methods applied to discounted cash flows, the internal rate of return (IRR) and the net present value (NPV) method, are used to measure the investments profitability. Level II charging installations (3.3 kW and 7.2 kW) are examined and the case focuses on a Greek facility thus, retail-tariffs contracts of the biggest Greek electricity retailer and EV penetration scenarios for Greece are considered. Results about the investment profitability are derived, followed by remarks on strategies that can increase facilitys profit. As the EVSCs market is still immature, a sensitivity analysis for various parameters is carried out to better support the economic results. The key profit determinants are identified.

Large-scale res integration in electricity markets: Challenges and potential solutions

The increasing shares of renewable energy in power systems have a significant impact on the operation of electricity markets and grids worldwide. This paper provides an overview of the main challenges that high shares of renewable generation introduce in the power system management and electricity markets operation as well as a brief description of potential solutions for alleviating the negative implications caused by the large-scale renewable integration. Additionally, a summary of the core research activity that has been performed in the context of a relevant academic research project, called “Large-Scale Renewable Integration in Electricity Markets”, (acronym, “LaRInEM”) is presented and valuable conclusions regarding the efficient integration of large amounts of renewable energy in electricity markets are drawn.

Artificial neural network-based methodology for short-term electric load scenario generation

In this paper a novel scenario generation methodology based on artificial neural networks (ANNs) is proposed. The methodology is able to create scenarios for various power system-related stochastic variables. Scenario reduction methodologies can then be applied to effectively reduce the number of scenarios. An application of the methodology for the creation of short-term electric load scenarios for one day up to seven days ahead is presented. Test results on the real-world insular power system of Crete present the effectiveness of the proposed methodology.

Assessment of the impact of a battery energy storage system on the scheduling and operation of the insular power system of Crete

This paper examines the impact that the installation of battery energy storage systems (BESSs) has on the daily scheduling and operation of the insular power system of Crete. An optimization model for the integration of BESS in the day-ahead unit commitment problem of isolated power systems is developed and annual simulations are carried out for BESSs of different dimensioning regarding power, capacity and efficiency. Simulation results for load leveling and reserve provision from BESS are presented and thoroughly discussed.