Automated Negotiation for Peer-to-Peer Electricity Trading in Local Energy Markets
Abstract
:1. Introduction
2. Automated Negotiation Framework
2.1. Negotiation Protocol
- (Pre-negotiation): First, the parties (agents) define the issues to be negotiated and the associated possible (allowed) quantities for each of them.
- An agent A determines the offer he will propose during the first round to agent B. The offer consists in the quantities of electricity for each period () and the price ().
- Agent B receives the offer; accepts it or discards it. In the first scenario, the negotiation is over. In the second, he proposes a counteroffer by determining its preferred quantities for each issue.
- Agent A can either accept it, propose a new offer (in which case we go back two steps above), or close the negotiation (no deal).
- Once the negotiation is done, the trade is validated against the physical constraints of the power exchange network, verifying that the network can support the agreed energy transfer.
- The next day, the agents commit to their energy trade.
2.2. Agent Models
2.2.1. Buyer Agent Model
2.2.2. Seller Agent Model
Algorithm 1: Remove impossible offers from |
Inputs: |
Set of offers to be considered. Maximum battery capacity if no battery). Initial battery capacity at the start of the day (0 if no battery). Set of forecasted renewable energy production at each time period Ni. The maximum power of the conventional fossil fuel powered asset (0 if no conventional supply). Set of four electricity quantities that need to be self-supplied by the seller at each period Ni. |
Initialize: |
the space of outcomes (utility) for the considered set of offers . |
For do. |
. |
For i = 1 to 4 do. |
. |
if then. |
. |
Return |
3. Agents’ Electricity Negotiation
- The agent first determines its required self-consumption () for each of the four periods, as well as the different marginal costs for electricity supply (, ).
- The seller agent also determines the forecast for its distributed energy resource (DER) production, while the buyer agent determines the values, as well as the weights and .
- The agents compute the utility function for each of the possible offers from the sets of discrete quantities and determined by the market facilitator. Thus, each agent generates the set of possible outcomes .
- The agents sort the set of possible outcomes and each determine the threshold below which it will not accept any offer. is the utility of the reserved or least package an agent can concede. Thus, any package with a utility below will be discarded.
- Negotiations begin with an agent (say agent A) initiating and sending the first offer/bid to the opponent (agent B).
- Upon receiving the offer, agent B evaluates the utility of the offer and determines if the offer is first suitable or not; that’s above or not. Depending on its strategy, agent B will either accept the offer; or refuse the offer by proposing a new offer/bid etc. within the specified deadline, until a bargain is either reached or the negotiation is closed without a deal.
3.1. Negotiation Strategies
3.1.1. “Zero Intelligence” (ZI) Strategy
3.1.2. Linear Heuristic Strategy
3.1.3. Expert Agent Strategy
3.2. Case Study
3.2.1. Buyers’ Profiles
- Buyer 1 is a consumer with equal preference for the cost of electricity, as well as the quantity of electricity he receives, provided it is close to its electricity need . Hence, .
- Buyer 2 is a consumer who prefers having the amounts of electricity given by , irrespective of price. Hence, and .
- Buyer 3 is a consumer who is most concerned with price and will adjust consumption based on the price as this buyer does not want to pay much money for its electricity consumption. Hence, .
3.2.2. Seller Profile
- Cloudy day case where the solar PV installation produces a power given by kWh, respectively.
- Sunny day case with a PV production given by: .
4. Experimental Results
4.1. Negotiation Framework Implementation
4.2. Negotiation Strategies Comparison
5. Discussion
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Multi-Tier Matrix for Measuring Access to Household Electricity Supply | ||||||
---|---|---|---|---|---|---|
Consumption Description | Tier 1 | Tier 2 | Tier 3 | Tier 4 | Tier 5 | |
Peak Capacity | Power capacity ratings (W) | Min 3 W | Min 50 W | Min 200 W | Min 800 W | Min 2 kW |
Daily Energy consumption (Wh) | Min 12 Wh | Min 200 Wh | Min 1 kWh | Min 3.43 kWh | Min 8.2 kWh | |
Or Services | Lighting of 1000 lumen-h/day | Electric lights, air circulation, TV, and phone charging are possible | ||||
Duration | Hours per day | Min 4 h | Min 4 h | Min 8 h | Min 16 h | Min 23 h |
Hours per evening | Min 1 h | Min 2 h | Min 3 h | Min 4 h | Min 4 h |
Computation of Utility Matrix | Negotiation | |
---|---|---|
CPU Time (s) | 4.1 | 0.7 |
Memory (MB) | 18 | 12 |
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Share and Cite
Etukudor, C.; Couraud, B.; Robu, V.; Früh, W.-G.; Flynn, D.; Okereke, C. Automated Negotiation for Peer-to-Peer Electricity Trading in Local Energy Markets. Energies 2020, 13, 920. https://doi.org/10.3390/en13040920
Etukudor C, Couraud B, Robu V, Früh W-G, Flynn D, Okereke C. Automated Negotiation for Peer-to-Peer Electricity Trading in Local Energy Markets. Energies. 2020; 13(4):920. https://doi.org/10.3390/en13040920
Chicago/Turabian StyleEtukudor, Christie, Benoit Couraud, Valentin Robu, Wolf-Gerrit Früh, David Flynn, and Chinonso Okereke. 2020. "Automated Negotiation for Peer-to-Peer Electricity Trading in Local Energy Markets" Energies 13, no. 4: 920. https://doi.org/10.3390/en13040920