New Software Lets Homes Balance Power Bills, Comfort, and Grid Stability
Researchers have developed an energy management system that lets households optimize electricity use across competing priorities—saving money, staying comfortable, and reducing peak demand—while accounting for solar panel output unpredictability. The advance could unlock household-level demand flexibility that utilities and grid operators urgently need to stabilize electricity networks as renewable energy expands.
Originaltitel: Occupant-centric stochastic many-objective home energy management with PV uncertainty under Sweden’s tariff scheme
Svenska hushåll kan sänka elnätsbelastningen betydligt genom flexibel elförbrukning — men framför allt om styrsystemen förstår både väder och personliga önskemål samtidigt. Forskargruppen vid Umeå universitet utvecklade en hemenergihanteringssystem (HEMS) som hanterar osäkerhet i solcellsproduktion medan den balanserar fyra konkurrerande mål: lägre kostnad, mindre CO₂, större energioberoende och mindre topplastvariationer. I ett pilotprojekt med ett svenskt enfamiljshus reducerade systemet elkostnaderna med 9,5 procent, koldioxidutsläppen med 9,3 procent och topplasten med 52 procent jämfört med regelbaserade alternativ — allt utan att offra boendekomfort. Den stokastiska planeringen minskade felmarginalen med 33,3 procent gentemot deterministiska metoder. För leverantörer av energistyrning och nätoperatörer är resultatet relevant: occupant-centrisk optimering under svenska tariffer möjliggör skalbar flexibilitet på stadsnivå utan att tvinga bort användare genom rigid automation.
Encouraging households to spread electricity use throughout the day, aggregated shifting can lower coincident peaks and ease stress on urban grids. This elevates home energy management systems (HEMS) as a key enabling technology, since sustained peak reduction often requires coordinated control of household distributed energy resources (DERs). However, existing HEMS approaches face two major limitations. First, they inadequately accommodate heterogeneous occupant preferences, limiting occupant-centric scheduling, where energy should be dispatched to balance multiple and conflicting objectives while maintaining comfort. Second, they rarely incorporate photovoltaic (PV) generation uncertainty into occupant-centric scheduling; when multiple competing objectives and tightly coupled constraints are optimized simultaneously, forecast errors can lead to infeasible schedules, unexpected peak demand, and comfort violations. To address these challenges, this paper proposes a stochastic, many-objective, occupant-centric HEMS framework that explicitly represents PV generation uncertainty while accommodating and balancing heterogeneous occupant constraints and priorities. A case study of a Swedish detached house considers four occupant-oriented criteria: cost savings, environmental impact, energy autonomy, and peak-load mitigation. The results demonstrate that the proposed framework generates more robust schedules than the deterministic framework, reducing the mean absolute error by 33.3 %. Moreover, under equally weighted objectives, it outperforms the rule-based strategy by reducing electricity costs by 9.5 %, CO 2 emissions by 9.3 %, and peak demand by 52 %, while maintaining thermal comfort. The framework supports robust and occupant-centric residential energy management, thereby demonstrating its potential for scalable deployment to unlock demand-side flexibility at the community and city levels.