Christos K. Simoglou

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.

A unified unit commitment — Economic dispatch model for short-term power system scheduling under high wind energy penetration

This paper presents a unified unit commitment and economic dispatch tool for the short-term scheduling of a power system under high renewable penetration. The proposed model uses variable time resolution and scheduling horizon extended up to 36 hours ahead and produces robust real-time decisions making the short-term operation of the power system almost insensitive to RES forecast errors. The proposed model is tested for a monthly period on the Greek interconnected power system using real load and wind power data for two different wind penetration levels. Simulation results show that the proposed methodology allows for the accommodation of large amounts of wind energy into the short-term scheduling of the power system at minimum cost.

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.

Evaluation of the impact of RES integration on the Greek electricity market by mid-term simulation

This paper presents a yearly simulation model for the analysis of the impact of the large RES penetration in the energy production mix of Greece on the operation of the Greek electricity market. This impact is analyzed in terms of the electricity market clearing prices, the total CO2 emissions, the total payment of the consumers for the energy withdrawal and the RES uplift charge, imposed by the government for the payment of the feed-in tariff to the RES producers. A mid-term scheduling model based on the day-ahead market clearing algorithm of the Greek wholesale electricity market is used for the yearly simulation under ten different scenarios regarding the RES installed capacity (multiple RES technologies are considered). Valuable conclusions are drawn from the application of the proposed model for the year 2011.

Coordination of day-ahead scheduling with a stochastic weekly unit commitment for the efficient scheduling of slow-start thermal units

This paper addresses the problem of the coordination of the day-ahead scheduling with a stochastic weekly unit commitment for the efficient scheduling of slow-start thermal units. The solution of the 24-hour unit commitment may lead to cases, in which slow-start thermal units that are initially offline cannot be scheduled efficiently, due to their long start-up and minimum-up times as well as their large start-up costs. Thus, a new method is proposed, in which the day-ahead scheduling is coordinated with the solution of a weekly unit commitment. The latter is formulated and solved as a two-stage stochastic mixed-integer linear program, under various system and unit operating constraints, according to the provisions of the Greek Grid and Exchange Code. The stochastic parameter of the weekly unit commitment is the unit availability; thus, possible unit outages during the optimization period are taken into account. Test results from the implementation of the proposed method on the medium-scale Greek electricity market are presented.

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.

Electricity producer self-scheduling in day-ahead energy and reserves markets

This paper addresses the problem of electricity producer self-scheduling in day-ahead energy and reserves markets. The electricity producer self-scheduling problem is formulated as a mixed integer linear program (MILP). It provides a tool to enable a producer to allocate optimally his degree of involvement in each one of these markets so as to maximize his profits. Residual demand curves are used to model the effect of producerpsilas interactions with his competitors. Test results on a small test system address the effect of the reserves prices as well as the producerpsilas forward commitments on the market clearing prices and the producer profits.

Evaluation of the capacity credit of RES: The Greek case

This paper addresses the evaluation of the capacity credit of RES plants in the Greek power system. Five different RES technologies are considered, namely wind, PV, small hydro, biomass, and cogeneration. The methodology adopted uses widely accepted power system reliability indices, such as the loss of load probability (LOLP) and the loss of load expectation (LOLE), computed using the capacity outage probability table (COPT). A three-step procedure for the calculation of the capacity credit for all RES technologies using the effective load carrying capability (ELCC) metric is implemented. Using real historic operational data of the Greek power system, the capacity credit of all RES technologies for the year 2011 are derived. Sensitivity results regarding the effect of the initial installed capacity and the new entrant capacity on the capacity credit are also presented.

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.

Benefits of demand response on a wind power producer bidding strategy

This paper proposes an optimal bidding strategy for a wind producer who participates as price-maker in the day-ahead electricity market. In order to empower the wind producer against the challenges introduced by the intermittent nature of wind power and improve his position into the market, demand response schemes are considered to be incorporated into his portfolio. The producer objective is the maximization of the day-ahead profit in conjunction with the minimization of the anticipated real-time production imbalance charges. A two-stage stochastic bi-level optimization model has been formulated, where the uncertainty lies in the day-ahead wind production and load consumption. Simulations on the Greek power system prove the effectiveness of the proposed synergy.