Community Microgrids and Local Energy Management

Module: Sustainable Tech | Difficulty: Premium

LCOE

Market Clearing

Comparison

| Technology | Capacity | Cost ($/kWh) | Community Benefit | |-----------|----------|-------------|-------------------| | Solar PV | 10-100 kW | 0.03-0.05 | Bill reduction | | Battery storage | 5-50 kWh | 0.15-0.30 | Resilience | | EV charging | 10-50 kW | 0.05-0.10 | Transport | | Heat pump | 5-20 kW | 0.08-0.15 | Heating/cooling |

Python Implementation

import numpy as np

class CommunityEnergyManager:
    def microgrid_optimization(self, load, gen, storage_cap):
        n = len(load)
        dispatch = np.zeros(n)
        soc = np.zeros(n + 1)
        soc[0] = storage_cap * 0.5
        for t in range(n):
            net = load[t] - gen[t]
            if net < 0 and soc[t] < storage_cap:
                charge = min(-net, storage_cap - soc[t])
                dispatch[t] = charge
                soc[t+1] = soc[t] + charge
            elif net > 0 and soc[t] > 0:
                discharge = min(net, soc[t])
                dispatch[t] = -discharge
                soc[t+1] = soc[t] - discharge
            else:
                soc[t+1] = soc[t]
        return dispatch, soc

    def p2p_trading(self, prosumers, consumers):
        trades = []
        for p in prosumers:
            if p['surplus'] > 0:
                for c in consumers:
                    if c['deficit'] > 0 and p['price'] <= c['max_price']:
                        vol = min(p['surplus'], c['deficit'])
                        trades.append({'seller': p['id'], 'buyer': c['id'], 'vol': vol})
                        p['surplus'] -= vol
                        c['deficit'] -= vol
        return trades

Research Insight: Blockchain-based P2P trading reduces electricity costs by 15-25% while increasing local renewable utilization to 60-70%.