Source
doi:10.3390/en14144318 — original publication (opens in a new tab; the file is not redistributed)
Summary
Peer-reviewed data-driven simulation study (Energies, MDPI, Vol. 14, No. 14, art. 4318) that quantifies how much of a residential community’s energy demand can be met by exchanging surplus PV generation between prosumers (PV-equipped households) and consumers, and how that scales into a community-level self-sufficiency ratio. It extends the household photovoltaic self-consumption definition of Luthander et al. (2015) to the community scale, defining self-sufficiency as the share of community demand met on-site during PV generation hours, and a complementarity factor (CF) as the ratio of instantaneous community surplus to community deficit. LIT_034
The analysis uses real demand and PV generation from ~250 households in the Pecan Street / Dataport project (Austin, TX, ERCOT grid), July-August 2015, at 15-minute resolution, with bootstrap sampling to build communities of 20-100 households at varying prosumer ratios, battery integration, and user load-flexibility. Headline finding: with PV integration above 75%, energy exchange alone can make the community fully self-sufficient during peak generation hours (~11 a.m.-3 p.m.), but complementarity collapses toward evening; adding battery storage or user load adaptation in the 5-7 p.m. window lifts community self-sufficiency during generation hours by up to 17% and 5-10% respectively, to 83% and 71-76%. LIT_034 This is a US dataset — values are method-illustrative and order-of-magnitude, not NZ-calibrated.
Key claims
- claim: "Community self-sufficiency is defined as the share of PV generation directly consumed by the community during generation hours: phi_ss = integral of sum M(t)dt over prosumers, divided by integral of sum P(t)dt over all prosumers and consumers, where M(t) = min{P(t), G(t)+B(t)} is on-site-utilised generation."
source_location: "p.4-5, Eqs. (3) and (4), Section 3.1 Basic Definitions"
- claim: "The complementarity factor is defined as CF(%) = 100 * |S^C / D^C|, the ratio of instantaneous community energy surplus (S^C, from prosumers with negative net demand) to community deficit (D^C, from positive net demand); CF=100% indicates complete independence from a central aggregator, CF>100% indicates surplus beyond community need."
source_location: "p.4, Eq. (2), Section 3.1 Basic Definitions"
- claim: "Net household demand is L(t) = P(t) - G(t) - B(t), where P(t) is power demand, G(t) is PV generation, and B(t) is battery power (B(t)<0 charging, B(t)>0 discharging); line losses assumed negligible."
source_location: "p.4, Eq. (1), Section 3.1"
- claim: "Case-study dataset: 244 residential households in Austin, TX from the Pecan Street Project / Dataport, comprising 119 prosumers with PV and 125 consumers; July and August 2015 used as representative summer months at 15-min resolution; after cleaning, 7144 prosumer and 7492 consumer daily profiles (96 data points each) remained."
source_location: "p.7, Section 4.1 Case-Study Community Characteristics"
- claim: "Median PV peak power across prosumers was 3.95 kW (5th-95th quantile 1.8-6.5 kW), highest PV peak 11.0 kW; assumed one Tesla Powerwall per prosumer house (13.5 kWh capacity, 7 kW peak power), against an average daily solar generation per prosumer of ~15 kWh."
source_location: "p.6 (Section 3.4 Battery Modeling) and p.7 (Section 4.1)"
- claim: "Across an equal prosumer/consumer split with no storage or adaptation, the complementarity factor averaged over 100 simulations peaks at 12-1 p.m. where prosumer surplus equals 72.5% of total community demand; for 5-6 p.m. and 6-7 p.m. CF falls below 5% and 1% respectively; overall hourly average CF = 35.6%."
source_location: "p.11-12, Section 4.2 and Table 1 (Average row 35.6%)"
- claim: "Table 1 hourly community CF (equal prosumer/consumer, average of 100 experiments): 9-10 a.m. ~15-17%; 11-12 p.m. ~60-64%; 12-1 p.m. ~70-79% (highest); 2-3 p.m. ~48-52%; 3-4 p.m. ~31-32%; 4-5 p.m. ~15-16%; 5-6 p.m. ~4.5-5%; 6-7 p.m. ~0.8-0.9%."
source_location: "p.12, Table 1, CF(%) column"
- claim: "With at least 75% prosumer ratio and no storage, community surplus-deficit balancing could be fully achieved (CF>100%) during 11 a.m.-2 p.m.; at 100% prosumer ratio the highest CF reached 165% (75% PR) and 420% (100% PR) from 12 to 1 p.m."
source_location: "p.11 (Varied prosumers' ratios) and p.13 (Figure 10 discussion)"
- claim: "Maximum self-sufficiency improvement from full (100%) battery integration was 4.8%, 11.3%, 13.0%, and 17.0% for 25%, 50%, 75%, and 100% prosumer ratios respectively; with 100% PV-battery adoption ideal community self-sufficiency reaches almost 83% by storing 10 a.m.-3 p.m. surplus for later use."
source_location: "p.14, Section 4.3 Energy Storage Integration; Figure 11"
- claim: "User load adaptation (thermostat setpoint increase plus rescheduling deferrable EV and wet-appliance loads in the 5-7 p.m. window) raised self-sufficiency for 100% PR from 65.1% to 75.5% (a 10.4% increase); for 75% PR from 53.2% to 62.2% (9.0%); for 50% PR 39.3% to 44.7% (5.4%); for 25% PR 22.1% to 26.5% (4.4%)."
source_location: "p.15, Section 4.4 User Adaptation and Load Profile Change; Figure 12"
- claim: "Underlying AC demand-response potential (Hu et al. 2017 grey-box RC thermal model): AC power reductions of 25%, 31%, and 68% for a setpoint increase of 1 C, 1 C with pre-cooling, and 2 C with pre-cooling respectively, over a 2-h declining-generation window."
source_location: "p.6, Section 3.3 (Dynamic Energy Use Behavior and Load Profile Change)"
- claim: "In the absence of storage, user adaptation for load flexibility delivered self-sufficiency improvement equal to ~60% of what commercial battery storage offered, and 69% (75% PR) / 61% (100% PR) of battery's improvement at high PV integration."
source_location: "p.16 (User adaptation versus battery storage) and p.19 Conclusions"
- claim: "In a comparable Swiss P2P field study cited by the authors (Worner et al. 2019), consumers paid 0.19 CHF/kWh to peers for solar energy versus a 0.21 CHF/kWh utility price, while prosumers set prices averaging 0.13 CHF/kWh; this raised prosumer revenue by ~32% and saved consumers ~7% on bills."
source_location: "p.17, Section 4.5 (Market design impact)"Neobiome Intelligence relevance
This source supplies a formal, citable community self-sufficiency definition for the NI energy skill. The self-sufficiency ratio (Eq. 3-4) — share of community demand met on-site during PV generation hours — and the complementarity factor CF (Eq. 2) give NI two distinct, defensible metrics: a self-consumption-style ratio and a surplus/deficit balancing ratio, both at community rather than single-household scale. LIT_034 This directly resolves RT_011, which flagged the paper as the upstream definition of the ESR-style formula referenced in CR_003.
For D01 Renewable Energy & Storage, the paper quantifies the temporal-mismatch problem at the heart of NI’s energy modelling: PV surplus peaks midday (CF up to ~72.5% of demand at 12-1 p.m.) but collapses to <1% by 6-7 p.m., so a self-sufficiency figure is meaningless without a stated time window. LIT_034 It also gives NI a concrete sizing anchor — one Tesla Powerwall (13.5 kWh / 7 kW) per prosumer against ~15 kWh average daily generation — and an evidence-based ceiling: 100% PV + 100% battery lifts generation-hours self-sufficiency to ~83%, never to full annual autonomy. LIT_034
For I06 Resistance to External Shocks, the prosumer-ratio sensitivity (full community balancing needs ≥75% PV penetration midday) tells NI how much local generation density is required before a community is genuinely grid-independent during daylight. For I01 Financial & Economic Sufficiency, the cited Swiss P2P field study gives an order-of-magnitude trading economics signal (~32% prosumer revenue uplift, ~7% consumer saving) and frames why falling feed-in tariffs push communities toward exchange. For I07 Fulfilment of Basic Needs, the study shows demand-side adaptation (AC setpoint + deferrable load shifting) can substitute for ~60% of battery storage’s self-sufficiency gain — a low-capital lever NI should be able to model alongside hardware. LIT_034
Transfer caveat: every number here is from a US summer dataset (Austin, ERCOT, 15-min, Jul-Aug 2015). The formulas transfer directly; the values (CF magnitudes, 83% ceiling, PV peak distributions, cooling-dominated AC-DR savings) reflect a hot-summer cooling-load climate and must be recalibrated for NZ’s heating-dominated, lower-irradiance winters before use in any NI calculation. The authors themselves note the analysis covers one summer season and that seasonality is unmodelled. LIT_034
Research targets
Documents to retrieve
- Luthander, Widén, Nilsson & Palm (2015) — “Photovoltaic self-consumption in buildings: A review,” Applied Energy 142:80-94, doi:10.1016/j.apenergy.2014.12.028. The household self-consumption / self-sufficiency definition that LIT_034 extends to community scale; needed to confirm the canonical single-building formula NI builds on.
- Hu, Xiao & Wang (2017) — “Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model,” Applied Energy 207:324-335, doi:10.1016/j.apenergy.2017.05.099. Source of the 25%/31%/68% AC load-reduction figures used in the load-flexibility scenarios; the only document behind the demand-response numbers.
Research gaps
- National (all-NZ) PV self-consumption / community self-sufficiency benchmark at sub-hourly resolution to recalibrate the Afzalan CF and self-sufficiency formulas to NZ irradiance and load shapes — the Austin/ERCOT values are cooling-dominated and cannot be used directly. Candidate sources: EECA / TIMES-NZ load data, smart-meter datasets.
- National NZ residential demand-response / load-flexibility potential for heating-dominated loads (heat pumps, hot-water cylinders) — the NZ analogue to the AC setpoint/pre-cooling figures, which are summer-cooling specific and do not map onto NZ winter peaks.
- National NZ P2P / community energy-trading price and revenue evidence to localise the Swiss CHF figures (prosumer revenue uplift, consumer bill saving) for the I01 economic case.
Connections
Referenced by
SSI indicators (3): I01: Financial & Economic Self-Sufficiency · I06: Resistance to External Shocks · I07: Fulfilment of Basic Needs
Sources (2): LIT_072 · LIT_073
EDT domains (1): D01: Renewable Energy & Storage Systems