Investigatio de Strategia Managementi Efficacitatis Energeticae Domusiam basata super Plantas PV Distributas et ESS

1 Systema Domus Intellectalis Basatum in ZigBee

Cum continua technologiae computatricis et informationis controlis progressione, domus intellectales rapiditer evolverunt. Non solum functiones traditionales retinent, sed etiam utenti dispositivos domesticos commodius administrare possunt. Etiam extra domum, utentes internam statum remote monitorare possunt, facilitantes managementem efficiendi energiae domesticae et significanter vitam qualitatem augentes.

Hoc opus designat systema domus intellectalis basatum in ZigBee, constans ex tribus componentibus: rete domus, server domus, et terminale mobile. Systema est simplex, efficax, et altissime expansibile, cuius structura ostenditur in Figura 1.

1 Architectura Domus Intellectalis Basata in ZigBee
1.1 Rete Domus

Ut fundamentum centrale, rete domus connectit onera controllabilia ut nodos pro interna data transmissione et multi-energia managemente. Optando pro solutionibus wireless (ZigBee) super wired, flexibilitas, fides, et expansibilitas augmentantur. ZigBee, aedificatus super IEEE 802.15.4, offert costum parvum, potentiam, et complexitatem cum alta securitate. Chips eius afforabiles systematis hardware costum reducunt. Rete includit:

• Coordinatores: Administrant rete ZigBee (CC2530-basati, IAR-compilati), quae per topologiam directo-connectivam domus typicas copiant.

• Nodorum Terminales: Integrati cum metris/relais (ut sockets intelligentes), collectant data et executant iussa pro “control + monitoring” clausura.

1.2 Server Domus

Server agit ut “data-control core” systematis, tractans:

• Data Hub: Exchangens info inter ZigBee (per portam seriam) et terminales mobili (per Socket).

• Operation Monitoring: Sequitur status onerum, controlat commutationes, et conservat data electricitatis.

• Energy Efficiency Brain: Analyzat data onerum/photovoltaicarum ad optimizandum scheduling, claudens loop managementis energiae.

1.3 Terminale Mobile

Basatum in Android (Eclipse + Java), terminale permittit:

• Status Visibility: Display real-time de informatione electricitatis ab server pushata.

• Remote Control: Mittere iussa ad controlanda onera indirecte.

• Flexible Scheduling: Settare temporationes custom onerum (exempli gratia, pro pricing tempore-usu).

2 Design Managementis Efficiendi Energiae Domus
2.1 Architectura & Logica Systematis

Integrando “domus intellectalis + PV + storage energiae”, systema embeddit strategias efficientias in server, formans “collect → model → optimize” loop:

• Data Layer: Combinat data onerum et PV.

• Model Layer: Balancet usum PV, storage, et onera via schemata optimalia.

• Control Layer: Coordinat operationes PV/storage et scheduling onerum pro “cost-efficiency” goals (structura in Figura 2).

2.2 Componentes Nucleares & Cooperatio

Componentes key (PV arrays, batteries, inverters, server, onera) operantur ut:

• PV Arrays: MPPT-enable per inverters, transmitter real-time output ad server.

• Storage Energiae: Grid-connected, charging durante PV surplus et discharging durante shortages (metered pro grid interaction).

• Server: Links inverters/sockets, adjustans dispositiva per regulas efficiency ad optimizandum fluxum energiae.

2.3 Classificatio & Scheduling Onorum

Onora dividuntur in tres species pro scheduling drivente pricing tempore-usu:

• Critical Loads (exempli gratia, illuminatio): Tempus fixum, non-adjustabile.

• Adjustable Loads (exempli gratia, AC): Variabilis-demanda, potestas-tunabilis.

• Shiftable Loads (exempli gratia, lavandaria): Tempus-flexibilis, nucleum pro efficiency.

Server controlat shiftable loads per sockets intelligentes, decurtans culmina/replectans valles ad decurtandum costus et stabilizandum grid.

3 Model Mathematicus et Strategia Controlis pro Managemente Efficiendi Energiae Domus
3.1 Model Mathematicus pro Managemente Efficiendi Energiae Domus

Ad praecise managementem efficiendi energiae domus, model mathematicus pro total cost electricitatis constitui debet. Hoc opus usum facit de “daily” control cycle, dividens 24 horas in n equal time intervals. Discretizando problemata continuae (quando n est sufficienter magnum, unusquisque intervallus ad “micro-elementum,” appropinquitat, et variabiles constantes intra intervallum esse possunt). In t-th interval, ex dynamica aequilibrio “power oneris domus, power generationis photovoltaicae, power battery charging/discharging, et power grid interaction,” derivatur aequatio systematis power balance ut:

Intra t-th time interval, power variables definientur ut sequitur:

• P_G^t: Power grid interaction (positivum pro absorptionem power, negativum pro injectionem power);

• P_A^t: Total power oneris domesticum;

• P_b^t: Power battery charging/discharging (positivum pro discharge, negativum pro charging);

• P_PV^t: Power output photovoltaicus (PV) (influenter per irradiationem solaris, temperaturam, humiditatem, etc., et predictibilis per models forecasting power PV).

Systema PV domesticum operatur sub modello “self-consumption + surplus power grid-feeding”, ubi surplus electricitas generat revenue grid-feeding et generation PV qualificationem habet pro subsidia. Considerando pricing tempore-usu (rates peak altiores, rates off-peak minores), calculatur total cost electricitatis ut:Total Cost=Grid Purchase Cost−Grid-Feeding Revenue−PV Subsidies

Pro daily cycle discretizatus in n intervals, model total cost decomponi potest in summationem costum specificorum intervalorum, precise adaptans ad scenaria pricing dynamic.

In formula: C repraesentat total daily cost electricitatis domesticum; f_PV est unit price subsidii generationis power photovoltaicae; 24/n est duratio unius time interval.
Expressio f_t in Formula (2) est

In formula: f_C^t est price electricitatis pro usuari durante t-th time period, qui dividitur in peak-time electricity price et off-peak electricity price secundum diversa tempora; f_R est price electricitatis pro surplus electricitas fed into the grid. Valores f_C^t, f_R et f_PV at any moment of the day sunt omnes noti. Total power P_A^t oneris domesticum aequalis summae power omnium shiftable loads et aliorum onorum durante t-th time period.

In formula: P_L,i est power operantis i-th shiftable load; T_L,i est start-up time i-th shiftable load; Δt_i est duration operantis i-th shiftable load; [t_i^s, t_i^e] est range start-up time i-th shiftable load. P_L,i, Δt_i, t_i^s et t_i^e sunt omnes valores definiti.

Power electricus P_else,j^t aliorum onorum est notus, dum power shiftable loads mutat secundum diversa start-up times, et T_L,i est valor indeterminatus. Quando T_L,i est different, total power P_A^t oneris domesticum mutat, sic mutat total cost electricitatis domesticum C.

3.2 Strategia Controlis

Nucleus goal managementis efficiendi energiae domus est maximizationem beneficiorum economicorum, specificus translatus in constructionem functionis objectivi pro “minimizing total household electricity cost C”.

Basatum in shiftable load model et combinatum cum mechanismo pricing tempore-usu, adjusting start-up time T_L,i shiftable loads potest dynamically optimizare curvam total power oneris domesticum, reducing total cost from perspective timing consumptionis electricitatis.

Logica Coordinated Control for PV and Energy Storage

Pro generatione power photovoltaica (PV) et batteriis storage, formantur strategies controlis pro diversis temporibus:

• Periods Peak: Prioritize full consumptionem generationis power PV. Si output PV > power oneris, surplus electricitas feed into the grid for revenue. Si output PV < power oneris, battery prioritized for power supply (when battery state of charge > minimum value). When battery depleted, insufficient part supplemented by grid.

• Periods Off-Peak: Battery charged at maximum charging power for energy storage. All load electricitas supplied by grid, utilizing low-price off-peak electricitas to &ldquo;fill valley&rdquo; et store energy for peak periods.

Battery Constraints

Necessarium est simul considerare limites power charging/discharging et restrictions capacitatis battery ad construendum behaviora charging/discharging (specific constraints need to be supplemented with formulas/models, not fully presented in original text), ensuring equipment safety et system stability.

In Formula (6): P_b,max est maximum power charging/discharging battery; in Formula (7), SOC^t est state of charge (SOC) battery durante t-th time period; SOC_min est minimum value SOC battery; SOC_max est maximum value SOC battery.

According to control strategy, optimize and control charging/discharging power of energy storage battery. During peak period t ∈ [t₁, t₂], where t₁ is start time of electricity peak period and t₂ is end time of electricity peak period, discharge power of battery set as

During off-peak period t ∈ [1, t₁], discharge power of storage battery set as

Necessary to calculate state of charge (SOC) of storage battery. Relationship between SOC during charging and discharging process of storage battery and charging/discharging power is as follows:

Formula (10) describes relationship between SOC of storage battery and charging power during charging (here P_b^t < 0; Formula (11) describes during discharging (here P_b^t > 0. SOC^{t + 1} is SOC in t + 1th period; σ (self-discharge rate, nearly 0% for small time intervals), η_ch (charging efficiency), η_dis (discharging efficiency), and E_b,max (max capacity) are battery parameters. In summary, home energy efficiency optimization aims to minimize total electricity cost by determining shiftable loads' start times and energy storage charging/discharging power at each moment, stated as:

Objective function

Constraint conditions

4 Case Analysis

To verify effectiveness of proposed home energy efficiency management method, simulations and analyses conducted using household electrical equipment of typical household in Shanghai. Home energy efficiency management system consists of photovoltaic panels, batteries, inverter, home server, and household loads. System configuration parameters shown in Table 1.

Shanghai implements time-of-use electricity pricing for residential living electricity, with peak hours from 6:00 to 22:00 at 0.617 CNY/kWh, and off-peak hours from 22:00 to 6:00 next day at 0.307 CNY/kWh. Feed-in tariff for surplus PV electricity is 0.4048 CNY/kWh. Shanghai's photovoltaic power generation subsidies include national subsidy of 0.42 CNY/kWh and local subsidy of 0.4 CNY/kWh, totaling 0.82 CNY/kWh.

Assume that maximum charging-discharging power of battery is 1.5 kW; minimum state of charge (SOC) set to 0.2, and maximum is 0.9. Initial SOC (SOC¹) of battery set to 0.2; charging-discharging efficiency of battery is 0.9.

Set n = 144, dividing 24-hour day evenly into 144 time intervals, with each interval being 10 minutes. Figure 3 shows power generation curve of PV system on certain day. Operation parameters of household loads shown in Table 2, where washing machine and water heater are shiftable loads.

Based on above data in Table 2, Matlab used to conduct simulation study on optimal management of household loads. According to home energy efficiency management algorithm, optimal household electricity usage scheme determined to minimize total daily electricity cost. Simulation results shown in Figure 4.

Post simulations, minimum total household electricity cost occurs when T_Li1 = 133 and T_Li2 = 132 (washing machine starts at 22:00, water heater at 21:50).

Figure 4 shows daily cost curve. Curve 1 (no energy management) and Curve 2 (with load shifting and storage control) reveal: Negative costs mean grid-feed revenue + PV subsidies > grid costs. Post-6:00, PV growth cuts costs until 17:00 (PV drops to 0, grid supply raises costs), ending at C = -2.02 CNY at 24:00.

With energy management, Curve 2 shows: Post-0:00, battery charging (from grid) boosts costs fast. Post-17:00, battery supply slows cost growth vs Curve 1. Load shifting further reduces costs, ending at C = -4.10 CNY (2.08 CNY decrease). Simulations confirm algorithm works—cutting costs, improving economy, and achieving peak-shaving.

5. Conclusions

A ZigBee-based smart home system integrating PV, storage, and energy management is designed. A mathematical model and time-of-use pricing-based algorithm are built. Simulations show it optimizes storage charging/discharging and load timing, slashing costs, boosting benefits, and proving feasibility.