Day-Ahead Offering Strategy In The Market For Concentrating Solar Power Considering Thermoelectric Decoupling By A Compressed Air Energy Storage

Due to limited fossil fuel resources, a growing increase in energy demand and the need to maintain positive environmental effects, concentrating solar power (CSP) plant as a promising technology has driven the world to find new sustainable and competitive methods for energy production. The scheduling capability of a CSP plant equipped with thermal energy storage (TES) surpasses a photovoltaic (PV) unit and augments the sustainability of energy system performance. However, restricting CSP plant application compared to a PV plant due to its high investment is a challenging issue. This paper presents a model to assemble a combined heat and power (CHP) with a CSP plant for enhancing heat utilization and reduce the overall cost of the plant, thus, the CSP benefits proved by researches can be implemented more economically. Moreover, the compressed air energy storage (CAES) is used with a CSP-TES-CHP plant in order that the thermoelectric decoupling of the CHP be facilitated. Therefore, the virtual power plant (VPP) created is a suitable design for large power grids, which can trade heat and electricity in response to the market without restraint by thermoelectric constraint. Furthermore, the day-ahead offering strategy of the VPP is modeled as a mixed integer linear programming (MILP) problem with the goal of maximizing the profit in the market. The simulation results prove the efficiency of the proposed model. The proposed VPP has a 2% increase in profit and a maximum 6% increase in the market electricity price per day compared to the system without CAES

Wind-CSP short-term coordination by MILP approach

This paper is on the maximization of total profit in a day-ahead market for a price-taker producer needing a short-term scheduling for wind power plants coordination with concentrated solar power plants, having thermal energy storage systems. The optimization approach proposed for the maximization of profit is a mixed-integer linear programming problem. The approach considers not only transmission grid constraints, but also technical operating constraints on both wind and concentrated solar power plants. Then, an improved short-term scheduling coordination is provided due to the more accurate modelling presented in this paper. Computer simulation results based on data for the Iberian wind and concentrated solar power plants illustrate the coordination benefits and show the effectiveness of the approach

Calculating the profits of an economic MPC applied to CSP plants with thermal storage system

Electricity producers participating in a day-ahead energy market aim to maximize profits derived from electricity sales. The daily generation schedule has to be offered in advance, usually the previous day before a certain moment in time. The development of an economically-optimal generation schedule is the core of the generation scheduling problem. To solve this problem, renewable energy plant owners need, besides energy prices forecast, weather prediction. Among renewable energy sources, concentrated solar power (CSP) plants with thermal energy storage (TES) may find it easier to participate in electricity markets due to their semi-dispatchable generation. In any case, the limited accuracy of forecasting solar resource brings about the risk of penalties that may be imposed to CSP plants for deviation from the submitted schedule. This paper proposes a model-based predictive control (MPC) approach with an economic objective function to tackle the scheduling problem in CSP plants with TES. By this approach, the most recent forecast and the current status of plant can be used by the proposed economic MPC approach to reschedule the generation conveniently at regular time intervals. On the other hand, a more feasible generation schedule for the next day is performed at the appropriate time thanks to the use of short-term forecast. The proposed approach is applied, in a simulation context, to a 50 MW parabolic trough collector-based CSP plant with TES under the assumptions of perfect price forecasts and participation in the Spanish day-ahead energy market. A case study based on a half-year period to test several meteorological conditions is performed. In this study, an economic analysis is carried out using actual values of energy price, penalty cost, solar resource data and its day-ahead forecast. Results show an economic improvement in comparison with a traditional day-ahead scheduling strategy, especially in periods with a bad weather forecast. To overcome the lack of short-term weather forecast data for this study, a synthetic short-term predictor, whose accuracy level can be tuned by means of a parameter, is used. Sweeping this accuracy level between the situation with no forecast improvement and perfect shortterm forecast, the MPC strategy reaches an improvement in total profits during the six months period between 13.9% and 33.3% of the maximum room for improvement. This maximum ideal improvement is defined as the difference in profits between the MPC strategy with perfect forecasts and the dayahead scheduling strategy.This research has been supported by DPI2016-76493-C3-2-R Project of Ministerio de Economía y Competitividad (Spain). The authors would like to thank Acciona Energa S.A. for expressing interest in the projec

Bidding and Optimization Strategies for Wind-PV Systems in Electricity Markets

The variability in non-dispatchable power generation raises important challenges to the integration of renewable energy sources into the electricity power grid. This paper provides the coordinated trading of wind and photovoltaic energy to mitigate risks due to the wind and solar power variability, electricity prices, and financial penalties arising out the generation shortfall and surplus. The problem of wind-photovoltaic coordinated trading is formulated as a linear programming problem. The goal is to obtain the optimal bidding strategy that maximizes the total profit. The wind-photovoltaic coordinated operation is modeled and compared with the uncoordinated operation. A comparison of the models and relevant conclusions are drawn from an illustrative case study of the Iberian day-ahead electricity market

Short-term Self-Scheduling of Virtual Energy Hub Plant within Thermal Energy Market

Multicarrier energy systems create new challenges as well as opportunities in future energy systems. One of these challenges is the interaction among multiple energy systems and energy hubs in different energy markets. By the advent of the local thermal energy market in many countries, energy hubs' scheduling becomes more prominent. In this article, a new approach to energy hubs' scheduling is offered, called virtual energy hub (VEH). The proposed concept of the energy hub, which is named as the VEH in this article, is referred to as an architecture based on the energy hub concept beside the proposed self-scheduling approach. The VEH is operated based on the different energy carriers and facilities as well as maximizes its revenue by participating in the various local energy markets. The proposed VEH optimizes its revenue from participating in the electrical and thermal energy markets and by examining both local markets. Participation of a player in the energy markets by using the integrated point of view can be reached to a higher benefit and optimal operation of the facilities in comparison with independent energy systems. In a competitive energy market, a VEH optimizes its self-scheduling problem in order to maximize its benefit considering uncertainties related to renewable resources. To handle the problem under uncertainty, a nonprobabilistic information gap method is implemented in this study. The proposed model enables the VEH to pursue two different strategies concerning uncertainties, namely risk-averse strategy and risk-seeker strategy. For effective participation of the renewable-based VEH plant in the local energy market, a compressed air energy storage unit is used as a solution for the volatility of the wind power generation. Finally, the proposed model is applied to a test case, and the numerical results validate the proposed approach

SCADA Office Building Implementation in the Context of an Aggregator

This paper at first presents an aggregation model including optimization tools for optimal resource scheduling and aggregating, and then, it proposes a real implemented SCADA system in an office building for decision support techniques and participating in demand response events. The aggregator model controls and manages the consumption and generation of customers by establishing contract with them. The SCADA based office building presented in this paper is considered as a customer of proposed aggregation model. In the case study, a distribution network with 21 buses, including 20 consumers and 26 distributed generations, is proposed for the aggregator network, and optimal resource scheduling of aggregator, and performance of implemented SCADA system for the office building, will be surveyed. The scientific contribution of this paper is to address from an optimization-based aggregator model to a SCADA based customer.This work has received funding from the Projects: NetEffiCity (ANI|P2020 18015); FEDER Funds through COMPETE program; National Funds through FCT under project UID/EEA/00760/2013; H2020 DREAM-GO Project (Marie Sklodowska-Curie grant agreement No 641794).info:eu-repo/semantics/publishedVersio