Prediction of the Italian electricity price for smart grid applications

In this paper we address the problem of one day-ahead hourly electricity price forecast for smart grid applications. To this aim, we investigate the application of a number of predictive models for time-series, including methods based on empirical strategies frequently adopted in the smart grid community, Kalman Filters and Echo State Networks (ESNs). The considered methods have been suitably modified to address the electricity price forecast problem. Strategies based on daily re-adaptation of models’ parameters are taken into consideration as well. The predictive performance achieved by the considered models is assessed, and the methods are compared among each other on recent real data from the Italian electricity market. As a result of the comparison over three years data, ESN methods appear to provide the most accurate price predictions, which could imply significant economic savings in many smart grid activities, such as switching on power plants to support power generation from renewable sources, electric vehicle recharging or usage of household appliances

A novel bidding method for combined heat and power units in district heating systems

We propose a bidding method for the participation of combined heat and power (CHP) units in the day-ahead electricity market. More specifically, we consider a district heating system where heat can be produced by CHP units or heat-only units, e.g., gas or wood chip boilers. We use a mixed-integer linear program to determine the optimal operation of the portfolio of production units and storages on a daily basis. Based on the optimal production of subsets of units, we can derive the bidding prices and amounts of electricity offered by the CHP units for the day-ahead market. The novelty about our approach is that the prices are derived by iteratively replacing the production of heat-only units through CHP production. This results in an algorithm with a robust bidding strategy that does not increase the system costs even if the bids are not won. We analyze our method on a small realistic test case to illustrate our method and compare it with other bidding strategies from literature, which consider CHP units individually. The analysis shows that considering a portfolio of units in a district heating system and determining bids based on replacement of heat production of other units leads to better results

Operational planning and bidding for district heating systems with uncertain renewable energy production

In countries with an extended use of district heating (DH), the integrated operation of DH and power systems can increase the flexibility of the power system achieving a higher integration of renewable energy sources (RES). DH operators can not only provide flexibility to the power system by acting on the electricity market, but also profit from the situation to lower the overall system cost. However, the operational planning and bidding includes several uncertain components at the time of planning: electricity prices as well as heat and power production from RES. In this publication, we propose a planning method that supports DH operators by scheduling the production and creating bids for the day-ahead and balancing electricity markets. The method is based on stochastic programming and extends bidding strategies for virtual power plants to the DH application. The uncertain factors are considered explicitly through scenario generation. We apply our solution approach to a real case study in Denmark and perform an extensive analysis of the production and trading behaviour of the DH system. The analysis provides insights on how DH system can provide regulating power as well as the impact of uncertainties and renewable sources on the planning. Furthermore, the case study shows the benefit in terms of cost reductions from considering a portfolio of units and both markets to adapt to RES production and market states

Week Ahead Electricity Price Forecasting Using Artificial Bee Colony Optimized Extreme Learning Machine with Wavelet Decomposition

Electricity price forecasting is one of the more complex processes, due to its non-linearity and highly varying nature. However, in today\u27s deregulated market and smart grid environment, the forecasted price is one of the important data sources used by producers in the bidding process. It also helps the consumer know the hourly price in order to manage the monthly electricity price. In this paper, a novel electricity price forecasting method is presented, based on the Artificial Bee Colony optimized Extreme Learning Machine (ABC-ELM) with wavelet decomposition technique. This has been attempted with two different input data formats. Each data format is decomposed using wavelet decomposition, Daubechies Db4 at level 6; all the decomposed data are forecasted using the proposed method and aggregate is formed for the final prediction. This prediction has been attempted in three different electricity markets, in Finland, Switzerland and India. The forecasted values of the three different countries, using the proposed method are compared with various other methods, using graph plots and error metrics and the proposed method is found to provide better accuracy

Short-term electricity price point and probabilistic forecasts

Accurate short-term electricity price forecasts are essential to all electricity market participants. Generation companies adopt price forecasts to hedge generation shortage risks; load serving entities use price forecasts to purchase energy with low cost; and trading companies utilize price forecasts to arbitrage between markets. Currently, researches on point forecast mainly focus on exploring periodic patterns of electricity price in time domain. However, frequency domain enables us to identify more information within price data to facilitate forecast. Besides, price spike forecast has not been fully studied in the existing works. Therefore, we propose a short-term electricity price forecast framework that analyzes price data in frequency domain and consider price spike predictions. First, the variational mode decomposition is adopted to decompose price data into multiple band-limited modes. Then, the extended discrete Fourier transform is used to transform the decomposed price mode into frequency domain and perform normal price forecasts. In addition, we utilize the enhanced structure preserving oversampling and synthetic minority oversampling technique to oversample price spike cases to improve price spike forecast accuracy. In addition to point forecasts, market participants also need probabilistic forecasts to quantify prediction uncertainties. However, there are several shortcomings within current researches. Although wide prediction intervals satisfy reliability requirement, the over-width intervals incur market participants to derive conservative decisions. Besides, although electricity price data follow heteroscedasticity distribution, to reduce computation burden, many researchers assume that price data follow normal distribution. Therefore, to handle the above-mentioned deficiencies, we propose an optimal prediction interval method. 1) By considering both reliability and sharpness, we ensure the prediction interval has a narrow width without sacrificing reliability. 2) To avoid distribution assumptions, we utilize the quantile regression to estimate the bounds of prediction intervals. 3) Exploiting the versatile abilities, the extreme learning machine method is adopted to forecast prediction intervals. The effectiveness of proposed point and probabilistic forecast methods are justified by using actual price data from various electricity markets. Comparing with the predictions derived from other researches, numerical results show that our methods could provide accurate and stable forecast results under different market situations

Decentralized Demand Side Management with Rooftop PV in Residential Distribution Network

In the past extensive researches have been conducted on demand side management (DSM) program which aims at reducing peak loads and saving electricity cost. In this paper, we propose a framework to study decentralized household demand side management in a residential distribution network which consists of multiple smart homes with schedulable electrical appliances and some rooftop photovoltaic generation units. Each smart home makes individual appliance scheduling to optimize the electric energy cost according to the day-ahead forecast of electricity prices and its willingness for convenience sacrifice. Using the developed simulation model, we examine the performance of decentralized household DSM and study their impacts on the distribution network operation and renewable integration, in terms of utilization efficiency of rooftop PV generation, overall voltage deviation, real power loss, and possible reverse power flows.Comment: 5 pages, 7 figures, ISGT 2018 conferenc

Adaptive Real-Time Optimal Dispatch of Privately Owned Energy Storage Systems

In this thesis, a real-time optimal dispatching (RTOD) algorithm is developed by formulating a mixed integer linear programming problem to determine charging and discharging power set-points of a privately owned energy storage system (ESS) in a competitive electricity market. The objective of the optimization problem is to generate revenue by exploiting price volatility in the day-ahead/week-ahead market. Moreover, this thesis aims to evaluate and improve the usefulness of publicly available electricity market prices for RTOD of a privately owned ESS in a competitive electricity market by developing a new adaptive technique. The pre-dispatch and the corresponding ex-post hourly Ontario energy prices are employed as the forecasted and actual prices. A compressed air ESS unit is optimally sized and modeled for evaluations. The conventional RTOD algorithm is developed, and its sensitivity to price forecast inaccuracy is evaluated. It is demonstrated that the forecast inaccuracy of publicly available market prices significantly reduces the ESS revenue. Then, a new adaptive algorithm is proposed and evaluated which adapts the objective function of the optimization problem online based on historical market prices. The outcomes reveal that the proposed adaptive RTOD can significantly reduce the adverse impact of the price forecast inaccuracy on the ESS revenue by online calibration of the 24-h-ahead market prices using 24-h-behind market prices. Moreover, the concept of optimal weekly usage of cryogenic energy storage (CES) is introduced and compared with the common daily usage. The results reveal significant benefits of weekly usage of the CES as compared to the daily usage