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Multi-sector demand response for cost optimal energy transitions [1]

['Trevor Barnes', 'School Of Sustainable Energy Engineering', 'Simon Fraser University', 'Surrey', 'British Columbia', 'Kamran Tehranchi', 'Department Of Civil', 'Environmental Engineering', 'Stanford University', 'Stanford']

Date: 2026-05 

Demand response is consistently highlighted as an important solution in electricity planning literature to help reduce costs throughout the energy transition. We build on these results and investigate how demand response performs in multi-sector energy planning studies. We implement two different demand response pricing strategies, one that is energy carrier independent and one that is energy carrier specific, and apply it to the open-source PyPSA-USA energy system optimization model. A case study of California and New England is performed for a future 2030 energy system. System-level metrics and net-load characteristics are analyzed. The results show that both electrical and thermal demand response offer cost savings in capacity constrained systems by acting as a flexible resource. Moreover, pricing demand response separately for each energy carrier and energy sector is most effective in reducing system costs. Finally, thermal demand response significantly reduces service sector space heating capacity, providing investment cost savings. A sensitivity analysis indicates that significant cost savings can be achieved with minimal demand response adoption. We conclude by highlighting how the pricing strategy implemented favours short-term load shifting and discuss where applying this method is appropriate.

Competing interests: Bradley Reinholz and Malcolm Metcalfe are/were employees of Generac Power System at the time of submission. Generac Power Systems provided funding for this research. The remaining authors declare no competing interests.

Funding: This research was finally supported by Mitacs ( https://www.mitacs.ca/ ) and Generac Power Systems ( https://www.generac.com/ ) under Mitacs project IT33642 (to T.B. and T.N.). Generac Power Systems reviewed the final manuscript prior to submission to ensure company secrets remained confidential and that business-relevant intellectual property was protected before a public disclosure. Generac Power Systems’ review did not lead to the removal of any data or elements to reproduce results, in compliance with PLOS Climate’s Data Availability Policy. The funders, with the exception of the co-authors, had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability: All data and code are generated using PyPSA-USA v0.8.0 available at ( https://github.com/PyPSA/pypsa-usa ). Output network files and processed results are archived on Zenodo at ( https://doi.org/10.5281/zenodo.17373139 ) and ( https://doi.org/10.5281/zenodo.17382703 ). The code used to perform the analysis and generate the figures for this manuscript is available at ( https://github.com/trevorb1/pypsa-demand-response ) and archived on Zenodo at ( https://doi.org/10.5281/zenodo.17388023 ).

Copyright: © 2026 Barnes et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

In this study, we investigate the value multi-sector DR can provide to a system in near-term energy transitions. Specifically, we quantify the system-level benefits of electrical and thermal DR when implemented independently and together in a capacity expansion model with high temporal resolution. The remainder of this paper is organized as follows: first, we review existing energy planning literature that applies DR and highlight how this work contributes to the field. Next, we introduce the energy model and detail our DR implementation. Finally, we present the results and discuss the academic and policy implications of this work.

Multi-sector analysis refers to multiple energy sectors and energy carriers represented within a single model [ 7 , 8 ]. Leveraging interactions between these different sectors and carriers often leads to more cost efficient energy transitions [ 9 , 10 ]. Understanding the interactions that exist between DR and multi-sector analysis will be a valuable tool to cope with uncertain energy transition trends. For example, Carvallo et al. [ 11 ] review Integrated Resource Plans (IRPs) and highlight how consumer patterns no longer follow historical trends. Or, as highlighted in [ 12 , 13 ], historically summer peaking systems must be able to transition to dual peaking or winter peaking systems driven by the deployment of Heat Pumps (HPs). Introducing mechanisms to handle periods of demand side uncertainty across all energy carriers is paramount for a reliable future energy system.

(a) Shows the load and net-load profile in a system without demand response. (b) Shows how demand response can shift electricity throughout the day to reduce the peak net-load and ramping requirements.

(a) Shows the original demand profile, (b) shows the profile adjusted for load shedding, (c) shows the profile adjusted for valley filling, (c) shows the profile adjusted for load shifting. Adapted from [ 6 ].

DR programs are designed to incentivize customers to shift and/or shed energy loads. Fig 1 shows the primary mechanisms of DR; those being load shedding, valley filling, and load shifting. In increasingly electrified systems, these mechanisms have increased potential to reduce network strain and emissions through managing the net-load; that being the total load in the system minus generation from Variable Renewable Energy (VRE) resources. For example, DR can be used to align supply and demand variability across all sectors to smooth the net-load curve [ 4 ], stylistically represented in Fig 2 . Or flexible loads, such as Electric Vehicle (EV) charging, can be shifted to off-peak hours [ 5 ]. Capturing these sectoral interactions is critical to utilize DR to its full potential.

DSM can broadly be split into two categories; Energy Efficiency (EE) programs, and Demand Response (DR) programs. EE programs result in “a persistent and maintained reduction in energy and/or demand, as compared to baseline consumption” [ 3 ]. While DR programs are “the active reduction, increase, shift, or modulation of energy and/or demand on a limited time basis, as compared to baseline consumption, in response to a price/incentive payment or command signal” [ 3 ]. This work focuses on evaluating DR programs within energy planning research.

Demand Side Management (DSM) is a general term that describes the actions an end-user can make to modify their load profile. Within energy planning literature, DSM is becoming an increasingly critical solution to cost-efficiently reduce emissions and help alleviate strain on energy infrastructure [ 1 , 2 ]. Studying the interactions that exist between DSM solutions and the different energy carriers and sectors can reveal novel decarbonization pathways.

In addition to these contributions, we incorporate two features not often found in capacity expansion DR studies. Firstly, we directly shift end-use loads, rather than the energy consumed from each technology. For example, instead of shifting a HPs load on the electrical network, we directly shift the heating load which can be met through other (non-electric) heating technologies. Secondly, we incorporate metrics to evaluate the impact of DR on the net-load profile. These metrics are often overlooked in the energy planning literature, but can provide valuable insight on the impact of DR that goes beyond the cost and capacity results.

Within the literature, there is a gap to study the multi-sector interactions of DR programs in capacity expansion frameworks with high spatial and temporal resolution. We address this gap with the following contributions:

All of these studies highlight a few key considerations when performing multi-sector DR analysis. First, the need to capture service level thermal demands (space-heating, space-cooling) and EV deployment charging requirements is important. Second, capturing heating peaks due to weather variability is crucial, as these can occur when DR is most beneficial to reduce capital expenditures. Finally, bottlenecks within the electrical and natural gas network may affect DR effectiveness and should be modeled.

Capacity expansion studies that include multi-sector DR have been conducted by Satchwell et al. [ 28 ], Qadrdan et al. [ 37 ] and Dominkovic et al [ 38 ]. These studies show that space heating, space cooling, and electrical loads can all offer significant cost benefits to the system. This is driven by DR offsetting the need for new peaking natural gas power plants, resulting in lower capital and fixed expenditures on the electrical network. Results are shown to be sensitive to electrical transmission bottlenecks. Due to the coarse time resolutions used in these long-term studies, results could not relate back to net-load characteristics. Furthermore, the impacts of electrical and thermal DR applications are not studied individually.

Evaluating the impact multi-sector DR can have on future, heavily electrified, energy systems has been done by Specian et al. [ 13 ]. The authors investigate DSM solutions for future winter peaking energy systems in the USA. They show that many DSM solutions can work together to effectively smooth the electrical load profile. Moreover, they highlight that DR will be a key factor in managing peak winter heating loads in increasingly electrified systems. The authors conclude by stating the need for further analysis to capture the interactions between the electrical and natural gas network. Moreover, this work does not highlight how investment decisions interact with DR.

Understanding how electrical and natural gas networks interact while considering DR has been studied by Shams et al. [ 35 ] and Li et al. [ 36 ]. These studies develop multi-sector energy optimization models to do day-ahead scheduling problems that incorporate DR. The authors find that system costs are sensitive to bottlenecks in the natural gas network and that multi-sector DR improves energy security margins. Moreover, capturing the variability associated with weather patterns is essential to understand the full impact of DR. While these studies excellently highlight key uncertainties to take into account, the impacts of future energy system development is not studied.

Load participation has been studied by Gils [ 33 ] who evaluate the theoretical potential of DR within Europe while considering different energy carriers and energy sectors. Additionally, O’Connell et al. [ 34 ] develop a production cost model to study DR on thermo-electric loads. The authors place particular emphasis on implementing saturation and load recovery constraints by tagging individual loads. Both studies highlight the benefits of considering multi-sector DR, but do not evaluate the role of sector-specific DR within a capacity expansion framework.

Although less prevalent than electrical only DR, there are studies that focus on multi-sector DR. These can range from the study of what loads can participate in DR, to investigating the impact of DR on heavily electrified systems that include fuel switching.

Plotted is the peak daily absolute 3hr net-load ramp value. The highest value represents the routine ramping requirement. The difference between the peak value and the highest value represent the extreme ramping value. Example net-load data is extracted from the 2022 load in the CAISO region of the USA [ 32 ].

To measure the need for load shifting, net-load ramping characteristics are measured [ 30 ]. Routine ramping measures the maximum daily three-hour net-load ramp on demand on the day with the highest ramp. Extreme ramping measures the maximum annual three-hour net-load ramp compared to the highest ramping day. These metrics are shown in Fig 4 . A lower extreme ramping represents a reduced need for short-term load shifting, while a lower routine ramping represents a reduced need for consistent load shifting.

Highlighted is the peak yearly net-load and the peakiness, which represents the difference between the peak net-load and the highest peak net-load. Example net-load data is extracted from the 2022 load in the CAISO region of the USA [ 32 ].

To measure the need for load shedding, system demand peaks are measured [ 30 ]. Peak net-load measures the maximum hourly system net-load over the year while Peakiness measures the height of system peak net-load about the 100th highest hourly net-load. This is graphically represented in Fig 3 . A lower peak load represents a reduced need for short-term load shedding. A lower peakiness value represents a reduced need for consistent load shedding. These metrics are important because the system’s tolerance to load shedding is minimal; with a typical loss of load expectation set at 0.1 days per year in the United States of America (USA) [ 31 ].

Metrics to measure system-level impacts of DR generally include studying investments in capacity, emission reductions, and costs [ 4 , 27 – 29 ]. Common findings show that DR provides valuable energy services by increasing the flexibility of the system through acting as a storage medium. DR provides capacity services by reducing the need for peaking plants (e.g., gas turbines) and deferring capital expenditures. Finally, DR can help facilitate the deployment of VRE by shifting demand to match supply over short time intervals.

Summarized by Nolan and O’Malley [ 16 ] (and supplemented by [ 25 , 26 ]), is that the effectiveness of DR programs can be measured against a baseline load profile. This is “the electricity that would have been consumed by a customer in the absence of a demand response event” [ 16 ]. To do this, a model is first run without DR to obtain a baseline result. DR is then implemented and the results are compared.

Incentive- and price-based programs have both been implemented in energy planning models. The state-of-the-art equations used in incentive-based programs are well summarized by Morales-Espana et al. [ 6 ]. The authors highlight methods to expand idealized load shifting constraints to include other parameters, such as saturation and load recovery, and provide constraints specific to certain loads, such as EVs. Alternatively, price-based implementations focus on the DR pricing strategy. For example, Miri et al. [ 21 ] highlight how Locational Marginal Prices (LMPs) can be used to implement a real time pricing scheme, Dranka et al. [ 22 ] use historical spot prices to set seasonal DR rates, or Mallapragada et al. [ 23 ] (who build on [ 24 ]) showcase a DR strategy that allows load to be shed once different price set-points are exceeded. Regardless of the implementation, studies consistently highlight the importance of including DR in energy planning models.

DR has a wealth of literature studying the various programs, benefits, and challenges (see [ 14 – 20 ] for review articles). In general, DR can be split into two categories; incentive-based and price-based. Incentive-based programs have customers opt into a program where the utility or aggregator can control a certain amount of their load in exchange for reduced payments or other incentives. Examples of this type of program include direct load control, scheduled load reduction, interruptible loads, or curtailable loads. Price-based programs have customers voluntarily modify their load in response to economic signals (i.e., price changes of energy). Examples of this type of program include Time of Use (TOU) rates, real-time pricing, critical peak pricing, and extreme day pricing.

We first highlight the different types of DR programs that have been implemented within energy planning literature. Next, we discuss suggestions from the literature on how to measure the effectiveness of DR programs. Finally, we summarize the work that has been done on multi-sector DR analysis.

For reporting purposes, each model run is given a unique scenario name. An overview of the 39 scenario names for each pricing strategy is given in Table 1 . California and New England follow the same naming conventions. All runs are repeated for both the carrier-average and carrier-specific DR pricing methods. This results in 78 runs per region, or 156 runs total.

Both California and New England are heavily dependent on importing natural gas to provide energy and capacity services throughout the year. Coupled with the fact that cumulative emissions within the USA have been heavily linked to the price of natural gas [ 52 ], we investigate low, medium and high levels of natural gas prices. The medium price is the average monthly price of citygate natural gas for the importing state, taken from EIA [ 46 ]. The high price is double the cost, while the low price is half the cost. The profiles of natural gas prices do not change, only the magnitude.

For each case we select four prices to iterate over; a high, medium, low, and very low cost of load shifting. The medium cost is the average annual marginal cost, as given in Fig 5 . The high cost is 150% this rate, the low cost is 50% this rate, and the very low cost is 25% this rate. We repeat this for both the carrier-average and carrier-specific pricing strategies. All prices for each scenario are given in S1 Appendix Table A.

Four options are implemented for scenario investigation along the DR sector dimension. First, a baseline multi-sector run is performed without any DR. Next, a run with only electrical DR is performed. Next, a run with only thermal (heating and cooling) DR is performed. Finally, an electric and thermal DR run is performed. All sectors participate in DR in each scenario. This dimension isolates the impact each energy carrier can provide.

A common method to handle uncertainty in energy planning studies is through scenario analysis [ 50 , 51 ]. This section will describe the dimensions used in our scenario design; those being demand response sector, demand response cost, and natural gas cost.

Two pricing strategies are investigated to evaluate the potential benefits of multi-sector DR. First, we model a carrier-average pricing strategy, where all energy carriers within a sector incur the same cost to shift load. This represents a simplified pricing scheme that aims to remove the complexity of end-users having to optimize their response against competing price signals. Second, we model a carrier-specific pricing strategy, where each energy carrier within each sector has a separate cost to shift load.

To price DR we use the annual average marginal price of energy. This is found by first running the model without DR to extract the average annual shadow price for each energy carrier, as shown in Fig 5 . This value represents the demand side willingness-to-pay to defer energy loads. We acknowledge that a real-world willingness-to-pay value will have temporal effects that are not captured by a flat DR rate, however, this rate acts as a neutral reference cost. The model will treat any marginal cost above this setpoint as having cost-saving potential. This approach allows us to isolate the benefits of multi-sector DR without conflating the results with the setup of the pricing scheme. Further work to optimize the cost function is described in Future work .

PyPSA is formulated as a linear program that minimizes system costs according to Equation 1 . In general, it states that the objective is to minimize the summed capital costs and operational costs of the system. DR is modeled as a store that allows energy to be shifted from one timestep to another, similar to that done in [ 22 ] for only electrical loads. To shift load, the model will incur a cost to store a unit of energy for each hour it is deferred (that is $/MWh/hr), following Equation 2 . This represents a willingness-to-pay for DR. Specific PyPSA implementation details are given in S1 Appendix .

1. Incentive-based programs benefit from loads that are classified by type. This allows DR specific constraints, such as allowable shift times, to be implemented. Studies, such as in [ 4 , 33 , 47 , 48 ], provide methods and data to help complete this process. However, due to the aggregation methods and limitations of the raw load metadata, classifying the loads by individual end-use in PyPSA-USA is not feasible.

For this study, we implement a DR module in PyPSA-USA. As discussed, both price-based and incentive-based programs have been implemented in the literature. We implement a price-based program for two reasons:

We enforce additional constraints on the base PyPSA-USA network to capture operational and investment restrictions unique to our research question. Electricity and natural gas networks are capacity constrained by current infrastructure and cannot be expanded. The annual import and export volumes of electricity and natural gas are restricted to not exceed current levels. Import and export costs of electricity are the state-level monthly wholesale price, as taken from the EIA [ 32 ]. When importing electricity, the model incurs an emission penalty equal to current level grid emissions. Investments in supply capacity are allowed for solar, wind (onshore and offshore), and natural gas (Open Cycle Gas Turbine (OCGT) and Combined Cycle Gas Turbine (CCGT)). The build rates for these technologies match the maximum 2030 build rates from EIA Annual Energy Outlook (AEO) [ 46 ] and include land use limits. There are no capacity limits on end-use technologies (such as HPs and furnaces) with the exception of oil furnaces, which are not extendable.

PyPSA-USA generates load profiles for all of the residential, commercial, industrial, and transport sectors. Residential and commercial loads for electrical, space heating, water heating and space cooling originate from the National Laboratory of the Rockies (NLR) ResStock and ComStock datasets [ 42 ]. Industrial electrical and heating loads are based on the Energy Information Agency (EIA) Manufacturing Energy Consumption Survey [ 32 ], Electric Power Research Institute (EPRI) Industrial Load Shape Library [ 43 ], and the NLR County Level Industrial Energy Use dataset [ 44 ]. Finally, transportation loads are based on the NLR Electrification Futures Study [ 45 ].

PyPSA-USA supports both spatial and temporal aggregation methods. This study uses hourly resolution to perform a capacity expansion and economic dispatch study of a 2030 planning year. Two regions are run independently; California due to its heavy investment in VRE, and New England (Maine, Massachusetts, Vermont, New Hampshire, Connecticut, and Rhode Island) due to its cold winters which can impact the deployment of HPs and EVs. California is run with 4 load zones and 80 renewable potential zones, while New England is run with 6 load zones and 120 renewable potential zones. A load zone refers to a region where the load is aggregated, while a renewable potential zone refers to a region where VRE assets are aggregated. A single load zone can have multiple renewable potential zones to capture multiple generation profiles from weather dependent assets.

In this study we evaluate if multi-sector DR is an effective mechanism to improve future energy systems based on the metrics described in the literature review. To conduct our analysis, we use PyPSA-USA to study future 2030 energy systems. A near-term target is selected as high-resolution load profiles for all energy sectors becomes increasingly uncertain for distant futures. Moreover, a near-term target allows us to capture existing end-use stock (furnaces, heat pumps, etc.) to study tradeoffs associated with replacing these technologies. We restrict investment in transmission lines and natural gas pipelines to highlight the impacts of DR in constrained systems. We run two different regional systems at high-temporal resolution to capture geographic differences and demand and weather variability. Finally, we model multiple end-use fuel demands across multiple energy sectors to understand how energy transition trends in different sectors will impact DR.

Py thon for P ower S ystem A nalysis (PyPSA) is a reputable open-source energy planning framework [ 39 ]. It is formulated as a linear cost optimization model and can be used for electricity and energy system capacity expansion planning and economic dispatch studies. PyPSA-USA [ 40 ] is an open-source and open-data model that can build configurable energy models of the USA on top of the PyPSA framework. It has full sectoral coverage and supports multiple end-use fuels. The reader is referred to S1 Appendix and PyPSA-USA’s documentation [ 41 ] for detailed supplementary information about the model.

We describe our process to evaluate the role of multi-sector DR within an energy planning framework. First, we give an overview of the energy planning model used in this study. Next, we describe the methodology to implement DR into the model. Finally, we present our scenario design.

Results

We present results by looking at system-level impacts (costs, capacity, and emissions), net-load characteristics, and DR specific metrics separately. A sensitivity analysis on the impact of DR penetration is completed at the end of this section. Full results are provided in S2 Appendix.

System costs System level net-present value costs for the carrier-average pricing strategy in California and New England are given in Fig 6. These costs include capital expenditures and operating costs across all sectors. Three key trends can be observed from the figure. First, electrical DR does not offer cost savings in any scenario in California. This result is contrary to typical findings, as the literature consistently states that electrical DR is an effective mechanism to reduce costs. Secondly, cost savings can be significant (almost 40%) in some scenarios in New England. Finally, cost savings diminish as the cost of natural gas increases. PNG larger image

TIFF original image Download: Fig 6. System objective cost results. Heatmaps show the cost deviation from the reference case of no DR applied to the system. The carrier-average pricing strategy is shown, where each carrier incurs the same cost to shift load. Negative values indicate a cheaper system. (a) California. (b) New England. https://doi.org/10.1371/journal.pclm.0000918.g006 Before discussing cost results in detail, we note the cost accounting strategy used. To quantify the economic benefit to the grid, we report the realized cost, not the objective cost. This realized cost is calculated by subtracting the cost of DR from the objective cost. We do this as the cost DR incurs is not seen by the system operator, rather it is a penalty to shift load. This strategy amplifies cost trends, however, it can lead to minor artifacts. For example, in Fig 6b the system with only thermal DR (t-high-hgas) shows slightly more savings (0.1%) than the system with thermal and electrical DR (et-high-hgas). This discrepancy is due to similar DR usage between systems and rounding errors (in this case, in the third decimal place). Unadjusted costs are given in S2 Appendix Fig A. California has existing flexibility measures that economically out-compete electrical DR. These measures primarily consist of electrical imports and significant OCGT capacity backed by ample natural gas pipeline capacity. We demonstrate this by constructing a capacity constrained California system and present its costs in Fig 7. This new system removes the ability to import electricity, removes existing OCGT capacity, and relaxes supply side build constraints. Under these conditions, electrical DR now out competes the existing peaking capacity and acts as a flexibility mechanism to reduce costs and promote VRE integration. PNG larger image

TIFF original image Download: Fig 7. California capacity constrained system costs. Heatmaps show the cost deviation from the reference case of no DR applied to the system. The carrier-average pricing strategy is shown, where each carrier incurs the same cost to shift load. Negative values indicate a cheaper system. https://doi.org/10.1371/journal.pclm.0000918.g007 In contrast, New England’s system is constrained by transmission level capacity bottlenecks in the electrical and gas networks. Coupled with the fact that no new oil furnaces are permitted and that Air Source Heat Pumps (ASHPs) in the winter have a low Coefficient of Performance (COP), electrical DR acts as a flexibility mechanism to reduce costs. This flexibility is primarily within the residential and commercial sectors to shift short-term heating load peaks (further discussed in Capacity results). Key Result 1. Capacity-constrained systems are more likely to benefit from electrical DR following the methodology presented in this paper. Ideally, the cost savings of electrical DR and thermal DR will be additive when both are introduced to the same system, however, this behavior is not guaranteed. In New England (Fig 6b), thermal DR achieves the maximum observed cost savings, with electrical DR not adding or removing value from the system. Conversely, the capacity constrained California system (Fig 7), exhibits additive behavior. Here, incorporating both electrical and thermal DR are required to obtain the maximum observed cost savings. The composition of the system will determine whether electrical DR, thermal DR or both are needed to achieve maximum cost savings. Key Result 2. It is system dependent if electrical and thermal DR showcase additive behavior when studying system-level cost savings. Both solutions independently offer system-level cost savings. Increasing natural gas prices consistently leads to lower amounts of relative cost savings across all systems. However, it is system dependent if the reduction occurs from capital expenditures, operational expenditures, or both. In capacity constrained systems with limited fuel switching options, natural gas will often be forced to run. In these systems an increasing gas price increases operational cost savings, as shifting the same natural gas load will save more money relative in that system. The capacity constrained California system shows this with its increasing operational cost savings from 0.1% to 5.8% between the low gas scenario (et-vlow-lgas) and the high gas scenario (et-vlow-hgas). However, in the naturally capacity constrained New England system, other cost competitive fuels, such as heating oil, provide fuel switching opportunities. This results in a decrease in operational cost savings of 58.2% to 23.1% across the same scenarios, as natural gas is not forced to be used. Finally, if flexibility measures already exist, such as in the base California system, operational cost savings decrease from 64.8% to 27.8% as these other flexibility measures will be used and reliance on natural gas is reduced. Key Result 3. A higher price of natural gas results in a lower total relative amount of cost savings from DR. It is system dependent if the savings decrease is seen in the investment or operational costs. Fig 8 shows that pricing DR by carrier (carrier-specific pricing) is more cost effective compared to pricing all DR carriers at the same price (carrier-average pricing). With only minor exceptions (<0.4% difference), the carrier-specific strategy consistently yields greater cost savings. This is primarily due to the price difference between energy carriers; with electricity being significantly more expensive than heating and cooling (see Fig 5). A carrier-average pricing scheme effectively inflates the costs of shifting thermal loads, as it is weighted by the expensive electricity. In these cases, the system will often build capacity to meet consistent peaks within the thermal sector, rather than pay a to defer load where the penalty is weighted by the electrical cost. Since thermal load peaks can offer significant capital investment cost savings (see Capacity results), this generally results in a more expensive system. PNG larger image

TIFF original image Download: Fig 8. System costs for carrier-average and carrier-specific pricing strategies. Heatmaps show the cost deviation from the reference case of no DR applied to the system. Negative values indicate a cheaper system. Results for mid natural gas costs are given. The top row is carrier-average pricing, while the bottom row is carrier-specific pricing. (a) California. (b) New England. (c) Capacity constrained California. https://doi.org/10.1371/journal.pclm.0000918.g008 Key Result 4. The carrier-specific pricing strategy is more effective in reducing system costs compared to the carrier-average pricing strategy.

Capacity Thermal DR significantly reduces system costs by lowering the end-use heating capacity requirements in the service sector. In all systems, natural gas furnace and HP capacity reduces while still meeting the same heating energy load, as given in Fig 9. Notable is the extreme reduction in total HP capacity in New England (90%+ in some scenarios). While this reduction is amplified by specific structural assumptions, as next described, it effectively highlights the ability of thermal DR to act as a flexibility measure. PNG larger image

TIFF original image Download: Fig 9. Service sector heat pump and gas furnace capacity. Heatmaps show the total capacity deviation from the reference case of no DR applied to the system. Negative values indicate less capacity. Results for mid natural gas costs are given. The top row is heat pump capacity, while the bottom row is gas furnace capacity. (a) California. (b) New England. (c) Capacity Constrained California. https://doi.org/10.1371/journal.pclm.0000918.g009 The significant reduction in HP capacity is driven by the land-use constraints that govern its deployment in PyPSA-USA. While the model can invest in both ASHPs and Ground Source Heat Pumps (GSHPs), GSHP investment is constrained in urbanized areas due to ground-space requirements. This approach follows methodologies seen in similar PyPSA projects [9], with full implementation details provided in S1 Appendix. Within New England, maximizing resource efficiency is critical to meet its significant heating load in the winter. Due to natural gas pipeline and electrical transmission capacity constraints, combined with the inability to expand heating oil furnaces capacity, meeting this load is challenging. Without DR, the system overbuilds ASHP to satisfy the GSHP capacity constraint, securing access to the stable COP GSHPs offer. The introduction of thermal DR provides a method to shift load to adapt to the variable COP of ASHP, drastically reducing the amount of total HP capacity and reducing costs. In reality, system planners will not build redundant ASHP capacity just to satisfy a land-use constraint, but this outcome shows the lack of flexibility measures without DR for near term transitions. The alternative to building unused ASHP is simply shedding load, which is also not realistic as consumers need to heat their buildings. Thermal DR reduces system dependence on the stable COP GSHPs offer, and allows for the adaption to the variable ASHP COP to meet the same heating load. Furthermore, load shifting allows the system to work around capacity constraints in the natural gas network and maximize existing natural gas furnace capacity, which does not have a time varying efficiency. Key Result 5. Thermal DR is an effective solution to reduce the total end-use heating capacity in the service sector. Light-duty EV deployment does not vary much in any run (<%3). This is initially surprising, as light-duty EVs add considerable concentrated load to the electrical grid; usually in the evening when people arrive home from work [45]. However, the price-based DR program implemented in this paper incentivizes the system to balance loads as quickly as possible. For EVs, this means storing the load from the early evening to the overnight period is costly. Shorter term balancing, such as for service sector heating applications, offer higher value to the system; this insight is expanded upon in the Impact of short-term load shifting prioritization section. Key Result 6. The pricing strategy implemented does not hinder or incentivize the deployment of light-duty EV capacity. Physical batteries and DR, effectively a virtual battery, present an important tradeoff; a physical battery incurs investment costs with minimal operational costs, while DR has no investment cost but consistent operation costs. The base California system does not consider this tradeoff, as existing flexibility measures do not require additional battery capacity and no changes are observed. In contrast, New England shows that DR consistently replaces significant physical battery capacity. Here, DR offers a more economical method to navigate network bottlenecks, often driven by short-term load peaks rather than prolonged storage needs. The capacity constrained California system, shown in Fig 10, reveals more complex interactions. First, we observe a substitution effect. As DR costs decrease, physical battery capacity declines until a saturation point is reached. Interestingly, in scenarios with high DR costs (e-high- scenarios), the system expands its battery capacity alongside DR. This suggests a complementary relationship where DR and batteries can work together to produce cheaper systems with more flexible capacity. Second, thermal DR can only substitute so much battery capacity, with it never reaching the saturation point (i.e., the currently installed battery capacity). This is consistent with the limitations of thermal DR. That being, it can shift electrified thermal loads (for example heat pumps) but can not address non-thermal electrical loads (for example EVs) which physical batteries can. Therefore, the system must always retain some physical battery capacity if only thermal DR is introduced. PNG larger image

TIFF original image Download: Fig 10. Capacity constrained California battery capacity. Heatmaps show the total battery capacity (MWh) deviation from the reference case of no DR applied to the system. Negative values indicate less capacity. https://doi.org/10.1371/journal.pclm.0000918.g010 Key Result 7. There are tradeoffs between the investment cost of physical batteries and the operational cost of DR. Electric DR serves as an effective substitute for physical batteries, but is cost dependent if it fully displaces battery capacity. Thermal DR never reaches the saturation point to fully displace physical batteries.

Emissions In the absence of an emission target or price on emissions, DR may or may not drive down emissions, as shown in Fig 11. In both Californian systems, DR offers mild benefits (<4%) in emission reduction. In part, this is driven by California naturally reducing emissions without the help of DR by 2030. Their mild winters provide good opportunity for HP deployment to electrify heating loads, while their excellent natural resources support maximum VRE deployment. In these cases, DR helps manage costs of the system through balancing loads and capital deferral, but is restricted in its ability to further drive down emissions given supply side build constraints. PNG larger image

TIFF original image Download: Fig 11. Total carbon emissions. Heatmaps show the emission deviation from the reference case of no DR applied to the system. The carrier-average pricing strategy with mid natural gas costs are shown. Negative values indicate less emissions released. (a) California. (b) New England (c) Capacity constrained California. https://doi.org/10.1371/journal.pclm.0000918.g011 Conversely, in New England, the introduction of DR can increase emissions by up to almost 10% in some scenarios. This result is primarily driven by the increase in generation from gas and oil furnaces. Through shifting electrical load, fossil fuel furnaces can be run consistently throughout the year. Heating loads can be modulated to work around the poor COP of ASHPs and transmission level bottlenecks. This is favorable for the system as existing oil and natural gas furnaces can be run, instead of generating or importing additional electricity to run through newly invested HPs. Introducing thermal DR can show emission benefits, as load is shifted to eliminate the more expensive heating oil in favour of cheaper, and less emission intense, natural gas. Key Result 8. Supply side build constraints and existing end-use heating stock will impact if electrical and thermal DR can decrease emissions for near-term energy transitions.

Demand response metrics As discussed in the literature review, peakiness, routine ramping, and extreme ramping metrics can provide further insight into the impact of DR. However, PyPSA-USA optimizes based on a simplified economic dispatch model to remain computationally tractable and does not capture system inefficiencies. This led to no consistent patterns being identified across any scenario dimensions for these metrics. PyPSA-USA minimizes the total cost of the system by sizing the capacity to the maximum load and then acting economically rationally to dispatch generators. Although we run at hourly resolution, we do not capture full operational details. For example, we do not track generator status to perform Unit Commitment (UC) studies and capture startup and shutdown costs. Moreover, PyPSA-USA, as configured for this study, runs with perfect-foresight. This means that the model knows the weather patterns and demand profiles for the full year and does not have to account for additional flexibility. Without more rigid operational constraints, even high temporal capacity expansion studies (up to 1 hour) do not have the fidelity to capture DR specific metrics. Key Result 10. The economic dispatch model used in this study, even at hourly resolution, does not capture enough operational details to quantify the impact of DR on peakiness, routine ramping, and extreme ramping.

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[1] Url: https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0000918

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