A Short-Term Decision Model for Electricity Retailers: Electricity Procurement and Time-of-Use Pricing

Round 1

Reviewer 1 Report

This paper approaches the problem of optimal decision making process for an electricity retailer that aims to maximize the profit resulting from buying power from electricity generators and from the spot market and selling it to the end consumers. The approach is based on a robust optimization method that use statistical data for spot prices (minimal, nominal and maximal values) instead of predicted values.

The paper is well structured and well written. However, the authors should take into consideration the following issues:

-        Page 4, eqs. (4) and (5): Is time "t" the same in eqs. (4) and (5)?  In other words, knowing that in eq. (4) time "t" is expressed in hours, the same is valid for eq. (5)?

-        Page 6, lines 257-264: define the independent variables (i.e. the variables to be tuned) in the RMINLP problem.

-        Page 7, eq. (32): define x_t with respect to variables in the objective function (26).

-        Page 8, but valid for the general approach used by the authors: The reviewer expresses doubts on setting separate time period partitions for each consumer category. In this way, for each type of consumer, the prices are maximal / minimal during the period when the consumer records the on-peak / off-peak load. The reviewer considers that for all customers the reference to on-peak or off-peak hours should be unique, related to the system load profile. Thus, the contribution of each customer at on-peak hours is better mirrored. The on / mid / off - peak partitioning approach used by the authors could be responsible for the big difference in prices between industrial / commercial and residential customers.

Author Response

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Reviewer 2 Report

The paper is well-written in general. There are some minor points to be considered:

1) please discuss the novelty of the proposed approach in Abstract.

2) please discuss limitations and directions for further research in Conclusions.

Author Response

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Reviewer 3 Report

The topic is interesting and up-to-date. Structure of the paper is very well. Authors presented problem by using in my opinion enough literature review. Description of proposed model is also well presented.

The main part ot he paper is Chapter 4 - Case study. Proposed method can be implement in other case studies also and that is why in my opinion the paper is important from both scientific and technical point of view.

In my opinion the paper is ready to publish. For the future research it could be interesting to compare presented approach with others.

Author Response

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Reviewer 4 Report

This paper proposes a short-term decision model, based on robust optimization, for an electricity retailer to determine the electricity procurement and retail prices. The objective of the model is to maximize the expected profit of the retailer through optimizing electricity procurement strategy and electricity pricing scheme. A case study is presented to illustrate the performance of the model. The results show that the developed model is effective in increasing the expected profit of the retailer and in flattening the load profiles of customers.

 

As a general comment, I think the paper makes an interesting contribution to the literature by providing a new decision model. At the same time, I think the paper needs minor improvements before to be published in this journal.

Broad comments

1)      In the introduction the authors make a good description of different methods used to decide the optimal electricity procurement strategy and electricity retail prices. I would suggest the author to mention also the following articles where a simulation of real economy with electricity price and different forecasting methods are presented:

a.       Linda Ponta, Marco Raberto, Andrea Teglio, Silvano Cincotti, “An Agent-based Stock-flow Consistent Model of the Sustainable Transition in the Energy Sector”, Ecological Economics, Volume 145, Pages 274-300, 2018

b.       Rafał Weron,“Electricity price forecasting: A review of the state-of-the-art with a look into the future”, International Journal of Forecasting, Volume 30, Issue 4, Pages 1030–1081, 2014

c.       Silvano Cincotti, Giulia Gallo, Linda Ponta, Marco Raberto, “Modelling and forecasting of electricity spot-prices: Computational intelligence vs classical econometrics”, AI Communications, Volume 27, Issue 3, Pages 301-314, 2014

2)      In subsection 2.3, could the authors explain better how “N time periods” are chosen? As there is an hourly electricity price I am wondering why N is not equal to 24. Please explain better this concept.

3)      In section 3.2, if I have well understood, the authors do not consider the transportation’s cost of electricity. Why? Please explain.

4)      In subsection 4.1, please clarify if the data reported in table 1 are real data or if they are simulated in some way. Please, give some more details.

 

Specific comments

Line 123-124: Please check the sentence because it is not clear.

Equation 1,3,5,7, line 136, please put the superscript near the corresponding letter

Line 131: what does “habits” mean?

Please check the English style

Author Response

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