Off-grid PV systems are providing critical access to energy services for millions of people throughout the globe. However, optimum sizing of these PV systems still poses a challenge, as inadequate system sizing could result in low system reliability and/or high cost of electricity generated. This paper presents a hybrid method of sizing off-grid PV systems for undefined electricity consumption. It then compares this sizing with off-grid PV systems installed in Nigeria - ranked the most populous electricity-deficit country in the world. The yields of the installed off-grid PV systems are also simulated for four major cities in Nigeria. Results show that for the over 1.5MWp of off-grid PV systems installed in the country, there is a potential 1.11 - 3.04 MWh of unutilised surplus electricity, which can supply 2hours of green electricity to at least 2, 000 Tier-2 households during peak demand in the dry hot season. Thus, our hybrid model provides design and operational insight to off-grid PV system optimization.

Reliable access to electricity is still a challenge in many developing countries. Indeed, rural areas in sub-Saharan Africa and developing countries such as India still encounter frequent power outages. Local energy markets (LEMs) have emerged as a low-cost solution enabling prosumers with power supply systems such as solar PV to sell their surplus of energy to other members of the local community. This paper proposes a one-to-one automated negotiation framework for peer-to-peer (P2P) local trading of electricity. Our framework uses an autonomous agent model to capture the preferences of both an electricity seller (consumer) and buyer (small local generator or prosumer), in terms of price and electricity quantities to be traded in different periods throughout a day. We develop a bilateral negotiation framework based on the well-known Rubinstein alternating offers protocol, in which the quantity of electricity and the price for different periods are aggregated into daily packages and negotiated between the buyer and seller agent. The framework is then implemented experimentally, with buyers and sellers adopting different negotiation strategies based on negotiation concession algorithms, such as linear heuristic or Boulware. Results show that this framework and agents modelling allow prosumers to increase their revenue while providing electricity access to the community at low cost.

Electricity-Access is a growing concern in developing countries of Sub-Saharan Africa where approximately 563million people (8% of the global population) lack electricity. Many use fossil-fuel generators and/or solar-home-systems (SHSs) with storage. Given the observed excess and unutilized electricity generated from these SHSs, a novel community peer-To-peer (P2P) electricity-market using automated negotiations is presented as a solution to improve access to electricity. Software agents representing World Bank Tiers 1-4 electricity-Access households bilaterally negotiate electricity price and quantities applying five different negotiations heuristics. Simulation results show 65-100% trade of the excess generation; with Tiers 1-2 (low-income households) negotiating sufficient electricity (1-1.1kWh/day) at the least market-price of £0.35/kWh to move to Tier 3. Likewise, the Tier 3 household agent negotiated 1.5kWh/day sufficient to meet its needs. This novel electricitymarket model can help drive the United Nations goal of affordable, sustainable and modern energy for all by 2030.