Multistage Model via Integrated Wind Thermal Energy Storage

Prof. H. Khaloiea et al., and his collaborative research team from various countries (Shahid Bahonar University of Kerman, University of Bonab, Iran, University of Vaasa, Finland, Aalborg University, Denmark, University of Salerno, Italy, Faculty of Engineering of the University of Porto, Portugal) have reported a three-stage stochastic multi-objective offering framework or model-based upon a mixed-integer programming formulation for spinning reserve markets via Wind Thermal Energy Storage (WTES) generation were proposed.

The utilization of renewable energy resources would have become one of the most essential requirements in our human life. Renewable energy resources like wind, or other integrated resources, were widely considered in various power systems, in particularly for the self-schedule offering of the strategic problems. Renewable energy resources were further divided into five general groups viz., solar power, wind power, biomass, hydropower, and geothermal process [1].

In early 2000, wind power technology is a vital one and a significant share of the electricity supply needed by various globalized customers. About 17 gigawatts of worldwide customers were provided by wind turbines in 2000, while it has got increased to about 361 gigawatts in 2014 which shows an increase in the use of wind energy and also reflects the interest from various communities. Wind power intermittency is known to be one of the greatest challenges for wind power producers (WPPs). In spite of the benefits of wind power, still there are many challenges for the owners to participate in the de-regulated electricity markets [2].

The main decisions that are proposed for a three-stage offering strategy problem are:

Dealing with energy deviations in the balancing market
Commitment of the decisions for dispatchable energy sources
Offering curves for the generation of the company in the energy and spinning reserve markets

The Novelty of the Proposal:

Researchers were proposed a novelty in this contribution on the three-stage multi-objective framework of a WTES system and the Energy storage systems(ESS) in the electricity markets for determining the optimal participation are:

Proposing three-stage stochastic multi-objective offering framework or model based upon a mixed-integer programming formulation for offering the strategy problem of a WTES system. It also suggested the model with uncertain parameters like energy, spinning reserve as well as the wind power production via stochastic scenarios.
Designing a new pattern based upon the emission trading of the WTES system while the emission quota is taken into full consideration to adopts the most suitable strategy.
Presenting the physical connection based on the mathematical formulation for the proposed problem between the ESS system with both thermal and wind units for charging it.
Providing a participation model subsequently, to derive an appropriate offering curve for this spinning reserve market in the ESS system for both charging and discharging modes.

Figure 1. Wind and Thermal Power Generation Plants (Image Credit to SNB Team)

Making of Decision Framework:

Stage I: Initially, GenCo’s decisions were been split up into two groups:

The 1^st group expressed GenCo’s decision regarding the operation part scheduling of thermal system units and ESS. The overall schedule of the horizon will be determined in this stage, particularly, the status (on or off) for the thermal system units and the (charging and discharging) modes for the ESS system.
The 2^ndgroup focused on the decisions that concern the charging power for the ESS from three different sources, namely, wind and thermal units as well as the day-ahead (DA) energy market will be made. Overall decisions are made a preference to the ionization of stochastic variables, which are called here-and-now decisions.

Stage II: At this stage, the decisions are declared to designing the offering curves that should be executed from the system in the DA energy and spinning reserve markets. Decisions of the stage-I and stage-II are inter-related to each other. Hence, the final decisions are entitled to special care on here-and-now decisions.

Stage III: At this stage, the decisions of stochastic programming concerns the balancing market and the electrical energy deviations of the system in this market. Here, the imbalance costs are caused by some deviation of wind turbines and the revenue uplifting from reserve deployment will be calculated. It should be very notable in this the stage-III, that the decisions will be made after the realization of all stochastic variables (DA energy market, balancing market, spinning reserve market, and wind power). Finally, stage-III decisions are denominated as wait-and-see decisions.

The current issues were divided into three different case studies and were analyzed which includes two different decision-making schemes each [3]:

First Scheme: In this scheme, the proposed multi objective optimization framework will make no distinction between the goals of emission minimization and profit maximization for the WTES system, comparatively with the significance of both OFs is equivalent (w₁, w₂= 1).

Second Scheme: In this scheme, the main primary goal is to attain the maximum profit by participating in the electricity markets with the significance of maximizing profits would be considered three times higher than the emission minimization (w₁= 3 and w₂= 1).

Our SNB team have emphasized this research article to enrich our viewer’s knowledge about a three-stage stochastic multi-stage model based upon a mixed-integer programming formulation for spinning reserve markets via wind thermal energy storage and generation were proposed by the authors. The proposed framework was modeled via a scenario-based approach with the uncertain nature of various parameters. The various proposals have been concluded and they have reported are as follows viz., (i) Influence of the system’s decision-making attitude was proposed in this offering problem as the first stage decisions, especially with the status of thermal units. (ii)Determining the most favored solution after attaining the Pareto solution set in regard to various emission quotas will allow for the emission trading pattern and also, this approach is found to be economically beneficial for those societies with their capabilities. (iii) Significant increase in the expected profits of the system in both decision-making schemes have been utilized with the third offering structures. (iv) Considerably, more profitable in the spinning reserve market with the mere participation of the energy storage system was obtained than the participants with the energy market.

Hence, the scientific peoples would have been expanded the proposed offering strategy for a price-maker WTES producer will face and augment several challenges to the problem, in particular for the future research studies.

References

C. Philibert, Int Energy Agency(2018).
M. Taylor et al., Int Renew Energy Agency(2016).
H. Khaloiea et al., Appl Energy (2019). DOI:10.1016/j.apenergy.2019.114168.

--- Dr. Y. Sasikumar

School of Materials Science and Engineering,

Tianjin University of Technology, China

Email:sasikumar@163.com

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