“…[51]- [55] Similar tariffs might lead to different outcomes locally A B D [33], [45], [46], [56] Forecasts of individuals are more error-prone than forecast of aggregates B [57], [58] Correlation of behavior and subsequent control issues due to wrong (price) signals B [59], [60] Different tariffs in different parts of the distribution grid can lead to transmission system issues…”
Section: A Cmentioning
confidence: 99%
“…[5], [11], [33], [46]- [48], [72] Real-time markets may lead to lower energy prices, price volatility, uncertainty amongst consumers and imbalances of demand and supply C [75], [76] Changes of traditional roles and responsibilities, market-structural factors such as cost and risks, product definitions and communication of demand-side effects C [11], [77] Markets are required to be robust to systemic changes such as carbon prices, feed-in-tariffs for renewables, etc.…”
Section: D Ementioning
confidence: 99%
“…[11], [17], [41], [46], [49], [54], [55], [55], [63], [64], [71], [72], [79]- [82] Scalability issues of communication devices C [4], [11], [55], [71], [83] Metering without a centralized authority needs to be reliable and trusted…”
Section: D Ementioning
confidence: 99%
“…[4], [45], [46], [49] Interoperability between communication technologies C [4], [55], [64] Rigid energy market regulations D [7], [33], [45], [84], [85] Need to protect elderly, socially disadvantaged, and price sensitive customers D E [16], [46], [55], [86] Enforcement of law, as digital contracts may not be appropriately regulated…”
In recent years, power systems have undergone changes in technology and definition of the associated stakeholders. With the increase in distributed renewable generation and small-to medium-sized consumers starting to actively participate on the supply side, a suitable incorporation of decentralized agents into the power system is required. A promising scheme to support this shift is given by local electricity markets. These provide an opportunity to extend the liberal wholesale markets for electrical power found in Europe and the United States to the communal level. Compared to these more established markets, local electricity markets, however, neither have few practical implementations nor standardized frameworks. In order to fill this research gap and classify the types of local electricity markets, the presented paper therefore starts with the challenges that these markets attempt to solve. This is then extended to an analysis of the theoretical and practical background with a focus on these derived challenges. The theoretical background is provided in the form of an introduction to state-of-the-art models and the associated literature, whereas the practical background is provided in form of a summary of ongoing and recent projects on local electricity markets. As a result, this paper presents a foundation for future research and projects attempting to approach the here presented challenges in distribution of generation, integration of demand response, decentralization of markets and legal and social issues via local electricity markets.
“…[51]- [55] Similar tariffs might lead to different outcomes locally A B D [33], [45], [46], [56] Forecasts of individuals are more error-prone than forecast of aggregates B [57], [58] Correlation of behavior and subsequent control issues due to wrong (price) signals B [59], [60] Different tariffs in different parts of the distribution grid can lead to transmission system issues…”
Section: A Cmentioning
confidence: 99%
“…[5], [11], [33], [46]- [48], [72] Real-time markets may lead to lower energy prices, price volatility, uncertainty amongst consumers and imbalances of demand and supply C [75], [76] Changes of traditional roles and responsibilities, market-structural factors such as cost and risks, product definitions and communication of demand-side effects C [11], [77] Markets are required to be robust to systemic changes such as carbon prices, feed-in-tariffs for renewables, etc.…”
Section: D Ementioning
confidence: 99%
“…[11], [17], [41], [46], [49], [54], [55], [55], [63], [64], [71], [72], [79]- [82] Scalability issues of communication devices C [4], [11], [55], [71], [83] Metering without a centralized authority needs to be reliable and trusted…”
Section: D Ementioning
confidence: 99%
“…[4], [45], [46], [49] Interoperability between communication technologies C [4], [55], [64] Rigid energy market regulations D [7], [33], [45], [84], [85] Need to protect elderly, socially disadvantaged, and price sensitive customers D E [16], [46], [55], [86] Enforcement of law, as digital contracts may not be appropriately regulated…”
In recent years, power systems have undergone changes in technology and definition of the associated stakeholders. With the increase in distributed renewable generation and small-to medium-sized consumers starting to actively participate on the supply side, a suitable incorporation of decentralized agents into the power system is required. A promising scheme to support this shift is given by local electricity markets. These provide an opportunity to extend the liberal wholesale markets for electrical power found in Europe and the United States to the communal level. Compared to these more established markets, local electricity markets, however, neither have few practical implementations nor standardized frameworks. In order to fill this research gap and classify the types of local electricity markets, the presented paper therefore starts with the challenges that these markets attempt to solve. This is then extended to an analysis of the theoretical and practical background with a focus on these derived challenges. The theoretical background is provided in the form of an introduction to state-of-the-art models and the associated literature, whereas the practical background is provided in form of a summary of ongoing and recent projects on local electricity markets. As a result, this paper presents a foundation for future research and projects attempting to approach the here presented challenges in distribution of generation, integration of demand response, decentralization of markets and legal and social issues via local electricity markets.
“…Therefore, DER owners can have opportunities to participate in electricity markets as prosumers for grid services managed by a distribution system operator (DSO). Furthermore, the concept of peer-to-peer (P2P) energy trading has been proposed for further flexible and resilient local grid services under a more complex future power grid environment with prosumers, decentralized energy systems, and new generation and consumption patterns, as well as for increasing the benefits to DER owners [ 10 ]. This localized P2P trading is the most recent trend regarding the energy industry and adopting inclusive sustainable models.…”
The rapidly increasing expansion of distributed energy resources (DER), such as renewable energy systems and energy storage systems into the electric power system and the integration of advanced information and communication technologies enable DER owners to participate in the electricity market for grid services. For more efficient and reliable power system operation, the concept of peer-to-peer (P2P) energy trading has recently been proposed. The adoption of blockchain technology in P2P energy trading has been considered to be the most promising solution enabling secure smart contracts between prosumers and users. However, privacy concerns arise because the sensitive data and transaction records of the participants, i.e., the prosumers and the distribution system operator (DSO), become available to the blockchain nodes. Many efforts have been made to resolve this issue. A recent breakthrough in a P2P energy trading system on an Ethereum blockchain is that all bid values are encrypted using functional encryption and peer matching for trading is performed securely on these encrypted bids. Their protocol is based on a method that encodes integers to vectors and an algorithm that securely compares the ciphertexts of these vectors. However, the comparison method is not very efficient in terms of the range of possible bid values because the amount of computation grows linearly according to the size of this range. This paper addresses this challenge by proposing a new bid encoding algorithm called dual binary encoding, which dramatically reduces the amount of computation as it is only proportional to the square of the logarithm of the size of the encoding range. Moreover, we propose a practical mechanism for rebidding the remaining amount caused when the amounts from the two matching peers are not equal. Finally, the feasibility of the proposed method is evaluated by using a virtual energy trade testbed and a private Ethereum blockchain platform.
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