Modeling and Evaluating Beneficial Matches between Excess Renewable Power Generation and Non-Electric Heat Loads in Remote Alaska Microgrids

Round 1

Reviewer 1 Report

This work evaluates the beneficial matches between excess renewable generation and non-electric heat loads in remote Alaska. Three partner communities are used as the study cases. A decision-making method is developed. The best matches are given. It is a worthy study. However, there are several issues should be addressed before accept for publication:

1. Does this paper aim to develop a decision-making method or evaluate the match effects between excess renewable generation and non-electric heat loads in Alaska? This issue should be explained more clearly.
2. HOMER Pro is not a common software. Its full name should be given when it first appears in the abstract and main text. In addition, is that an optimization software or a modeling software?
3. “Heat load data is compiled or modeled for each community”, which software is used to model the heat load of community? Is the simulated load steady-state or transient?
4. The evaluation indicator (NRMSE) is a little one-sided, Equation (3). The power generation and heat demand are transient and volatile, so it is a little one-sided to look at the good or bad match only from the perspective of quantity. The quantity is equal, but the supply and demand may be opposite in the time.
5. Figure 2, the heat loads of Community 1 and Community 2 are constant. While, the actual heat loads are volatile in a day. Can you provide a matching analysis in smaller time scale, such as hourly?
6. A brief discussion is suggested on which type of heat pump is more suitable for this beneficial match in Alaska. For example, CO2 transcritical heat pump or other subcritical type?
7. The validations of prediction models and optimization method should be added. The applicable time scale of models and method should be discussed.

Author Response

Response: We thank the reviewer for going through the paper and taking the time to provide constructive feedback.

1. Does this paper aim to develop a decision-making method or evaluate the match effects between excess renewable generation and non-electric heat loads in Alaska? This issue should be explained more clearly.

Response: As stated in the fourth sentence of the abstract, “This paper develops a decision-making method TO evaluate the match effects.” To clarify, we altered the sentence in Paragraph 7 in the Introduction. 

1. HOMER Pro is not a common software. Its full name should be given when it first appears in the abstract and main text. In addition, is that an optimization software or a modeling software?

Response: This has been corrected, and HOMER is now spelled out both in the abstract as well as in the first instance of usage. Hybrid Optimization Model for  Multiple Electric Renewables (Homer) Pro is designed and promoted for simulation (modeling), optimization and sensitivity analysis.   From the HOMER PRO website: “HOMER is a simulation model. It will attempt to simulate a viable system for all possible combinations of the equipment that you wish to consider. Depending on how you set up your problem, HOMER may simulate hundreds or even thousands of systems. HOMER simulates the operation of a hybrid microgrid for an entire year, in time steps from one minute to one hour.” For this work, HOMER Pro is used for simulation of the hybrid microgrids, and not economic optimization., so we retain the words ‘model’ and  ‘modeling’.

 

1. “Heat load data is compiled or modeled for each community”, which software is used to model the heat load of community? Is the simulated load steady-state or transient?

Response:  For communities 1 and 2, the monthly heat load is compiled from publicly available building energy audit reports from the Alaska Housing Finance Corporation (AHFC) and paragraph 2 of section 2.2.2 has been edited slightly to clarify this. Following the reference to these reports, one can discover that the Alaska specific energy modeling software developed by AHFC, Akwarm, has been used to model the heat loads. For community 3, no reports were available and the heat load was modeled by the method presented in paragraph 3 on of section 2.2.2.  All load simulations are steady state, and this has been clarified by adding this wording to the end of the final paragraph of section 2.2.2.

1. The evaluation indicator (NRMSE) is a little one-sided, Equation (3). The power generation and heat demand are transient and volatile, so it is a little one-sided to look at the good or bad match only from the perspective of quantity. The quantity is equal, but the supply and demand may be opposite in the time.

Response:  Yes, NRMSE is one-sided. However,  the goal of the method is to evaluate how close the match is  between demand and supply, and not to compare which is higher or lower. So in this case the NRMSE method serves the goal of the study. Sentence 6 of the abstract, as well as the seventh paragraph of the Introduction, have been edited to reflect this.

1. Figure 2, the heat loads of Community 1 and Community 2 are constant. While, the actual heat loads are volatile in a day. Can you provide a matching analysis in smaller time scale, such as hourly?

Response: We only have monthly modeled heat loads for these communities, which we have interpolated to daily.  This is why the heat loads appear to be constant. We  have modified the last sentence of section 2.2.2 as follows to clarify this: “The modeled heat load data available from public sources for Communities 1 and 2 are only resolved monthly and these have been linearly interpolated to create estimated daily heat loads for the purposes of this study.”

 We realize this is an approximation of reality, and paragraph 2 of the Discussion discusses the desirability of having hourly data as well as the lack of available measured heat data at any scale.  We also have added a last sentence to the last paragraph in section 2 to further explain our choice of daily analysis: “The appropriate timescale for this analysis is a few hours to daily given the thermal storage inherent in water tanks and buildings.  Due to the resolution of the data available we use daily resolution in this analysis.”

1. A brief discussion is suggested on which type of heat pump is more suitable for this beneficial match in Alaska. For example, CO2 transcritical heat pump or other subcritical type?

Response: heat pumps are not the focus of this paper, we mention them for completeness on the heat load assumptions and for possible future work – at that time the specific technologies should be investigated.  

To clarify this, the text in the paper has been changed. Specifically, the first sentence of the last paragraph in the discussion has been changed to the following: 

“This paper focuses on electric resistance heat. However, if a heat pump were to be utilized instead of direct electric resistance heating, that heat pump technology would change the heat load curve used in the match by the temperature-dependent coefficient of performance at any point in time.”

1. The validations of prediction models and optimization method should be added. The applicable time scale of models and method should be discussed.

Response: More data from communities would be needed to validate the method.  A sentence has been added to paragraph 2 of the discussion: “Additional community data, especially from existing wind to heat systems, would also allow validation of this method.”

HOMER PRO is industry standard software and has been validated by renewable energy data and other numerical models (see Mehdi Baneshi, Farhad Hadianfard, Techno-economic feasibility of hybrid diesel/PV/wind/battery electricity generation systems for non-residential large electricity consumers under southern Iran climate conditions, Energy Conversion and Management, Volume 127, 2016, Pages 233-244, ISSN 0196-8904,https://doi.org/10.1016/j.enconman.2016.09.008.) A sentence has been added to the first paragraph of section 2.2.1 with this information.

The applicable time scale is multi-hour to daily given the thermal capacity inherent in the water and building heat loads studied.  This is stated in the last paragraph of section 2.2.2 but has also been added as this sentence after Equation 3:

“The appropriate timescale for this analysis is a few hours to daily given the thermal storage inherent in water tanks and buildings.  Due to the resolution of the data available we use daily resolution in this analysis.”

Reviewer 2 Report

Comments

This paper developed a method to determine whether a community has a good match between an excess renewable energy resource and a heat load, specifically heat loads like space heating, water heating and treatment, and clothes drying in three Alaska communities. It is doubtful for application because the only small communities were used, but it is worth of the new method for decision-making for the renewable energy. As this method gains more improvements and experience, it has the potential to apply for the decision-making for the renewable energy deployment in a reality.

1. Table 1: Communities 1, 2 and 3 have small 70-400 population. The author had better to simulate larger community with more population as well as increasing a number of communities, and their interaction between each community.
2. Equation 3: The RMSE is a function which gives 0 in the matching renewable energy with the demands. However, in a real problem, we should choose the lower cost renewable energy under the condition that the RMSE keeps less than 1.
3. Equation 3: This equation is imperfect, missing one parenthesis in the root.
4. Equation 3: Is this equation divided by total timesteps, N? It gives larger value as N increases.
5. 6 L.250-263: The authors should check table number: table 1 -> table 2
6. 7 L.267-271: The descriptions are lack of the discussion: The authors had better to describe the concrete causes for the calculation results of 0.6 or 2558.4 to point out the problems of renewable energy at this communities.
7. Figure 2: The authors should add graphs of another representative months, as showing the February data in Figure 2, because it is potential that the data of another month show different behavior.
8. 8 L.299: In community 2, the wind is seemed to be difficulty a better fit to the heat loads than solar.

Author Response

Response: We appreciate the reviewer going through the paper and providing useful comments. We have addressed the concerns raised by the reviewer. 

1. Table 1: Communities 1, 2 and 3 have small 70-400 population. The author had better to simulate larger community with more population as well as increasing a number of communities, and their interaction between each community.

Response: The communities selected and simulated are representative of remote Alaska communities, which is the emphasis and motivation for this paper.  Focusing on larger and more communities is an excellent idea for future research.   Paragraph 7 in the Introduction has wording altered to reflect this. “We then demonstrate the method's application in three remote and rural communities in Alaska. We chose communities that span a typical range in size and load profile, of varying populations and climatic conditions, to represent several common Alaskan conditions.”

1. Equation 3: The RMSE is a function which gives 0 in the matching renewable energy with the demands. However, in a real problem, we should choose the lower cost renewable energy under the condition that the RMSE keeps less than 1.

Response: We agree that this method only points out renewable generation and heat loads that should be investigated further for the technical and economic feasibility of the match with other methods.  The second to last sentence before eqn 3 is changed to: As the loads and generation vary at different time scales, this relationship may change slightly, but generally the NRMSE can be used to indicate beneficial matches with lower values (ideally less than one), which can be investigated with additional analysis to determine which is most feasible technically, socially, and economically. 

1. Equation 3: This equation is imperfect, missing one parenthesis in the root.

    Response: This has been corrected, thanks!

1. Equation 3: Is this equation divided by total timesteps, N? It gives larger value as N increases.

Response: Eqn. 3 is not divided by N. Yes, it would be larger if N were to increase. N is the total number of timesteps over the year.  As the analysis is done with the same N for each case (365 in the case of daily data), N does not increase during an analysis.  A final sentence in section 2 has been added to clarify this: “All calculations of NMRSE to compare matches between excess generation and heat loads must use the same timestep, i, and number of timesteps, N.”

 

1. 6 L.250-263: The authors should check table number: table 1 -> table 2

    Response: This has been corrected - thanks!

1. 7 L.267-271: The descriptions are lack of the discussion: The authors had better to describe the concrete causes for the calculation results of 0.6 or 2558.4 to point out the problems of renewable energy at this communities.

Response: Paragraph edited with added sentences to describe the concrete causes: “The best (lowest) NRMSE value calculated in this study was 0.6 for the beneficial match between wind generation and clothes drying heat loads in Community 2 and wind and space heating in Community 3. In both of these cases, the daily excess wind energy is often nearly the same magnitude as the heat load. The worst (highest) NRMSE value from this study is 2558.4 from the combination of solar generation and clothes drying in Community 2 (not shown). In this case, the clothes drying load is as high in magnitude as the winter space heating load, but is nearly constant throughout the year, and the excess solar generation evaluated is the lowest level (25% of the peak community electric load). The daily heat load is always much higher than the excess solar generation.” 

 

1. Figure 2: The authors should add graphs of another representative months, as showing the February data in Figure 2, because it is potential that the data of another month show different behavior.

Response: The purpose of Figure 2 is to show the fact that on a daily scale, the short term variability of the wind and solar resource may cause the excess generation to be above the heat load one day, and below it the next.  This two week period illustrates many of the variations in this behavior. This sentence has been added at the end of paragraph 3 of the Results section to help clarify this: “While the excess generation of the resource may be more likely to be adequate, day-to-day, in certain months, days of low wind or solar resource happen throughout the year. The two week period chosen for Figure 2 illustrates many of the variations of this behavior.”

1. 8 L.299: In community 2, the wind is seemed to be difficulty a better fit to the heat loads than solar.

Response: Yes, in community 2, wind is a better fit to heat load than solar. This is indicated in paragraph 2 of the Results section.

Round 2

Reviewer 1 Report

This paper can be published.