## 5 Category I: Energy Transaction
### Buyer-Seller Matching
#### 5.2.3 Pricing Mechanism  
The pricing scheme in energy system is very important, it is also related to the system performance. The first thing we need to address is the privacy issue in the pricing process. The secure Multi-Party Computation (MPC) is a way to solve the problem of privacy protection in pricing-related interactions. It can solve the collaborative computing problem of protecting privacy among a group of participantswho do not trust each other, and ensure the independence of input and the correctness of calculation without disclosing their input values. For this reason, a bidding protocol based on secure MPC is proposed in [79], allowing mutually distrustful parties to compute without disclosing fine-grained electricity consumption data. The trade price and bids selection are performed in a distributed, oblivious manner.The billing protocol is based on a simple privacy-friendly aggregation technique that can prevent the detailed consumption data from leaking to suppliers when they calculate customers' monthly bills. In the actual pricing process, some economic concepts are used to help build the model. In 1993, [PERSON] and [PERSON] [99] proposed a concept of \"zero-intelligence\" (ZI) trader in their research and suggested that the convergence to the theoretical equilibrium price is largely determined by the market structure rather than the intelligence of traders. Since then, the concept of \"zero-intelligence plus\" (ZIP) traders has been introduced based on ZI traders [100]. Similar to ZI traders, these simple agents do random bidding, but ZIP traders use a basic form of machine learning that is closer to human traders in terms of performance. This concept of trader is also being used as an agent to join distributed energy trading systems. In [89], ZIP traders participate as agents in an auction mechanism that automatically adjusts profit margins within specific budget constraints. After the wide application of the distributed generation system integration technology of renewable energy, we also need to consider the issue of direct transaction pricing between small-scale electricity suppliers and end-users. In [101], the authors considered this issue, making use of the coalitional game theory to realize the pricing scheme with fair distribution of benefits among small power suppliers and end users. In practical projects, many effective pricing mechanisms have been implemented to encourage renewable energy production. In Germany's Grunstromjeton project [69], for example, smart meters calculate the share of electricity generated by renewable energy in a user's total electricity consumption. Users with higher share of renewable energy will be rewarded with a currency called Gruenstromjeton, which has real value. Similarly, France's I-NUK project [48] will also give small solar energy producers higher prices to promote the development of the clean energy. In addition to the basic transaction pricing, there are many other aspects to be considered in a pricing mechanism, such as the maximization of system performance. For example, there are some pricing algorithms based on game theory. Through comparison and evaluation of multiple pricing mechanisms, it is concluded that the pricing mechanisms based on game theory can achieve the best local energy scheduling [102]. In [103], an optimal loan pricing strategy in a credit-based payment scheme is proposed, which makes it possible for buyers who do not have enough coins to buy energy. It also computes the best count of loan for each borrower and the penalty rate through a noncooperative Stackelberg game. Stackelberg game theory is also used in [104; 105], in which the service demand pricing of miners is considered from the perspectives of unified pricing and discriminatory pricing respectively, both of which are calculated to determine the best price for profit maximization. While most pricing mechanisms take into account the optimization of system benefits, [PERSON] et al.