Discussion Paper:
Grid Singularity’s Implementation of Symbiotic Energy Markets

Abstract

I. Introduction

Grid Singularity is implementing Symbiotic Energy Markets in stages, adding or adapting modules to its codebase to take advantage of advancements in blockchain technology, increasing regulatory interest to enable bottom-up markets and the proliferation of connected energy resources. We rely on open source innovation and ecosystem collaboration to develop an inclusive, efficient, accountable and secure energy exchange:

  • to all stakeholders, providing barrier-free access [1] for all market actors (established and new) including individuals and their communities, grid operators and utilities, trading algorithm providers, aggregators/energy management service providers, regulators, as well as diverse energy assets;
  • to any individual choice, allowing participants to trade and interact with energy based on their personal preferences [1] on a wide choice of attributes.
  • technically, structured modularly and without matching and arbitration turning into a computational burden [8] for scenarios with many participants;
  • organisationally, allowing democratic deliberation and decisions on system upgrades;

II. Grid Singularity Exchange Functionality

A. Validated Market Participation

To be inclusive, the energy market infrastructure should facilitate unhindered onboarding and interoperability of many different asset types and participants with various technology levels, which entails standardising the interfaces and registration process.

B. Degrees of Freedom

In the Symbiotic Energy Markets concept, bids and offers for energy include a set of attributes (e.g. encrypted unique asset ID, asset location, type of energy produced e.g. solar energy, or membership in energy club) and diverse requirements that reflect trading preferences, such as energy quantity, price range, energy source, geographic distance, or preferred trading partner.

Figure 1. Grid Singularity Exchange: data flows from energy assets over aggregators and trading agents via APIs for matching. Energy Web Switchboard is to be used as a decentralised asset registry.
Figure 2. An example of a bid and offer with attributes and requirements. The bid submits three sets of conditions. As the second condition is fulfilled by the offer, the two orders are successfully matched, in this case for 0.8 kWh of photovoltaic (PV) energy (at a price between 21 and 25 cents as determined by the matching algorithm). A verification function performs this check. The function accepts a bid / offer pair as input, returns a <True> (green check) if there is a valid match, and returns a <False> (red x) if requirements are not met. If the function returns <True>, a trade is created. In a near-term centralised implementation, this verification function is integrated into the exchange code. In a blockchain implementation, it is to be deployed as a module of the parachain’s protocol.
  • Bids and offers can be matched one-to-many, allowing e.g. a single bid to fulfill multiple offers in the same operation, improving liquidity in the market and reducing the number of calls to the verification contract.
  • Defining `max energy` and multiple sets of conditions allows bids and offers to specify an energy mix, e.g. 30% PV, 30% from a preferred trading partner, and 40% grey energy.

C. Matching and Arbitration

Multi-attribute auctions require more advanced clearing algorithms than single attribute auctions (e.g. stock market). Previous researchers, such as [2], propose a linear programming approach with clearing intervals to match multi-attribute price-energy-temporal requirements. However, in a blockchain implementation, the computational cost would make this approach unfeasible at scale. This has been shown to be solved by having (potentially untrusted) off-chain workers run matching algorithms requiring either the use of trusted execution environments (enclaves) [5] or an on-chain verification which determines if the matching algorithm was run correctly [2]. Both conclude, however, that multiple off-chain workers would be required to check the validity of the solution vs. an agreed upon matching algorithm and arbitration technique.

Figure 3. Flow diagram of the role of mycos (third party matching algorithm providers) in the Grid Singularity Exchange.

D. Time Slots — Spot, Futures, Settlement, and Balancing Markets

  • Spot and Futures Markets
Figure 4. Spot, futures, and settlement markets allow trading centered around energy delivery.
Figure 5. Bids and offers must select a time slot as a requirement, specifying during which time the energy requested or provided must be delivered.
  • Settlement Market
Figure 6. Energy deviations (difference between purchased and delivered energy) can be traded in the settlement market instead of paying a penalty to the grid operator, reducing inefficient accounting. Any remaining deviation can still be penalised by the grid operator.
  • Clearing Intervals
  • Balancing Market
  • Option type: demand (consumption) or supply (production)
  • Power: minimum and maximum power required to be supplied by the balancing provider
  • Location: balancing group or physical location
  • Price / kWh: the agreed price of the delivered energy, and the premium for the option
  • Delivery: periods of time that specify the power delivery profile
  • Validity period: the period of time the option purchaser has to exercise the option
  • Mode of Activation: automatic or manually activated
Figure 7. Configuration requirements of a balancing market option for transmission system operators and other parties to procure balancing services. The option is matched by mycos and validated by the verification function of the Grid Singularity Exchange.

E. Grid Topology Representation

We consider grid topology because its disregard would result in a market platform without specific objectives and local trading would not be prioritised [16]. The power grid has been modelled using elements of graph theory and complex network theory [27], [28], resembling scale-free graphs, where a few nodes are highly-connected, constituting the hubs of large clustered groups of isolated nodes, resisting individual node failure or attack [29], i.e. forming independent, self-sufficient microgrids.

Figure 8. Left: representation of the grid network as a graph. Nodes here represent transformers or interconnection points where markets are most likely to be placed (blue circles) and energy assets (purple diamonds). Weights can be assigned to nodes to reflect symmetrical nodal grid fees, or to edges to represent the cost to trade across a specific line. Right: adjacency list representation of the same graph used to store on-chain.

F. Dynamic Grid Fees

Grid Singularity Exchange aims to account digitally for the physical exchange of energy occurring in the grid, which is independent of individual trading preferences and has physical limitations. The grid’s frequency and voltage, network line losses, and other metrics must be balanced. Residential customers’ energy bills typically include the energy consumed and a grid fee to cover network operation [33], namely balancing the discrepancies between the accounted energy and the physical power flow. To address network constraints, [22] suggests that distribution operators could directly modify or cancel orders, while [2] applies physical safety constraints (e.g. consumption or production limits) to the clearing mechanism.

Figure 9. Time-dependency and space-dependency of nodal grid fees. The letters W, X, Y, Z represent grid fees for their respective nodes at two subsequent time steps.

G. Decentralised Blockchain Architecture

Blockchain architectures allow market participants to post bids and offers and be matched on-chain with smart contract verifications [16], [18]. Grid Singularity’s future blockchain implementation will incorporate the identity authentication from Energy Web Switchboard (as described above) to verify participants and use the data for settlement, while its exchange is likely to be established as a parachain on a Substrate-based network (e.g. Polkadot). Notably, neither Polkadot or the Energy Web Chain rely on a proof-of-work mechanism of validation and therefore do not overuse electricity.

Figure 11. Information flow between aggregator, Grid Singularity exchange and matching algorithm provider (to be sourced through Data and Algorithm Marketplace). IPFS or RDBMS will likely be used for off-chain storage of the order book. The matching algorithms will be run off-chain or in an instance of SubstraTEE. The Grid Singularity Exchange will operate as a pallet on a Substrate-based parachain or another network that provides the best security and privacy combined with execution efficiency.

H. Privacy and Security

Individual energy data should remain anonymous to secure the system and its participants. [2], for instance, proposes how billing could be organised without prosumers disclosing anything other than the billed amount. Prosumers could use anonymous accounts when posting offers, but as [5] stressed they would still reveal their usage profile, which is personal data. [5] and other researchers have presented alternative concepts that provide confidentiality of personal data while still delivering transparency of the auction process, which Grid Singularity will consider and integrate in the exchange development.

I. Data and Algorithm Marketplace

The analysis of market and grid data can provide valuable input:

  • historical data and signals, energy usage predictions, and network analyses;
  • trading or matching algorithms for aggregators and mycos, grid management monitoring and grid fee strategies;
  • community or grid optimisations using historical data in simulations;
  • creative representations of energy interactions between participants used in social or artistic endeavours; and/or
  • processing of data for regulatory audit.

J. Transformation of energy market roles

New or adapted business models will rapidly grow and mature with the introduction of local energy markets powered by exchanges like Grid Singularity, allowing diverse participants to be rewarded for value-adding contributions to the market:

  • Prosumers
  • Aggregators
  • Exchange Operators and Network Validators
  • Mycos (third party matching algorithm providers)
  • Algorithm and Data Providers
  • Grid Operators
  • Utilities

III. Path Forward

While this paper specifies the blueprint of the Symbiotic Energy Market, its implementation will be modular and progressive, accounting for regulation, community or grid operator standards and preferences in a four-stage development process:

  1. Decouple matching from transaction validation and enable a spectrum of energy choices (degrees of freedom)
  2. Integrate a futures and settlement market
  3. Integrate a balancing market including demand and supply options and direct energy asset management
  4. Enact organic, accurate grid topology and dynamic grid management mechanisms
  • Business models and reward functions for market participants and service providers,
  • Selection and long-term operation of mycos, especially in large multi-nodal networks,
  • Analyses of market efficiency through community and individual key performance indicators (KPIs), as discussed in [40],
  • Alignment and synchronicity of the modular components of the exchange, e.g. ability to annul active bids or offers,
  • Maximising privacy and security of the exchange,
  • Algorithm design for matching, verification, trading, and grid management,
  • Additional mechanisms for grid operators to reward the distributed optimisation of the physical grid.

References

  1. Mazzola, Luca, Alexander Denzler, and Ramon Christen. “Towards a Peer-to-Peer Energy Market: an Overview.” arXiv preprint arXiv:2003.07940 (2020).
  2. Scott, Eisele, et al. “Safe and Private Forward-Trading Platform for Transactive Microgrids.” 2019.
  3. Mengelkamp, Esther, et al. “Trading on local energy markets: A comparison of market designs and bidding strategies.” 2017 14th International Conference on the European Energy Market (EEM). IEEE, 2017.
  4. Kumar, Dinesh, et al. “A systematic study of double auction mechanisms in cloud computing.” Journal of Systems and Software 125 (2017): 234–255.
  5. Brenzikofer, Alain, and Noa Melchior. “Privacy-Preserving P2P Energy Market on the Blockchain.” 2019.
  6. Tushar, Wayes, et al. “Peer-to-Peer Trading in Electricity Networks: An Overview.” IEEE Transactions on Smart Grids, vol. 11, no. 4, 2020, pp. 3185–3200.
  7. BEUC — The European Consumer Organisation. Energy markets of the future: How the EU’s energy transition should work for consumers. 2017.
  8. Sousa, Tiago, et al. “Peer-to-peer and community-based markets: A comprehensive review.” Renewable and Sustainable Energy Reviews, vol. 104, 2019, pp. 367–378.
  9. Alliance for Internet of Things Innovation. “Open Energy Marketplaces Evolution — Beyond Enabling Technologies”. 2021.
  10. Z. Elmrabet, et al. “Cyber-Security in Smart Grid: Survey and Challenges”
  11. Balogh, Lilla Sarolta. “Az igazsággépezet: Michael J. Casey–Paul Vigna: The Truth Machine–The Blockchain and the Future of Everything St. Martin’s Press, New York, 2018, 302 o.” Közgazdasági Szemle 66.9 (2019): 1005–1009.
  12. Mühle, Alexander, et al. “A survey on essential components of a self-sovereign identity.” Computer Science Review 30 (2018): 80–86.
  13. Miyamoto, Toshiyuki, et al. “An energy trading system with consideration of CO 2 emissions.” IEEJ Transactions on Electronics, Information and Systems 125.10 (2005): 1513–1521.
  14. Gimpel, Henner, and Juho Mäkiö. “Towards multi‐attribute double auctions for financial markets.” Electronic Markets 16.2 (2006): 130–139.
  15. Nassiri-Mofakham, Faria, et al. “Bidding strategy for agents in multi-attribute combinatorial double auction.” Expert Systems with Applications 42.6 (2015): 3268–3295.
  16. Mengelkamp, Esther, et al. “Designing microgrid energy markets: A case study: The Brooklyn Microgrid.” Applied Energy 210 (2018): 870–880.
  17. Beiler, Kevin J., et al. “Architecture of the wood‐wide web: Rhizopogon spp. genets link multiple Douglas‐fir cohorts.” New Phytologist 185.2 (2010): 543–553.
  18. M. Mihaylov, S. Jurado, K. Van Moffaert, N. Avellana, and A. Nowe,´“NRG-X-Change — A novel mechanism for trading of renewable energy in smart grids,” in SMARTGREENS, 2014, pp. 101–106.
  19. Märkle-Huß, Joscha, Stefan Feuerriegel, and Dirk Neumann. “Contract durations in the electricity market: Causal impact of 15 min trading on the EPEX SPOT market.” Energy Economics 69 (2018): 367–378.
  20. Khuntia, Swasti R., Jose L. Rueda, and Mart AMM Van der Meijden. “Long-term electricity load forecasting considering volatility using multiplicative error model.” energies 11.12 (2018): 3308.
  21. Lindberg, K. B., et al. “Long-term electricity load forecasting: Current and future trends.” Utilities Policy 58 (2019): 102–119.
  22. Zhang, Chenghua, et al. “Peer-to-Peer energy trading in a Microgrid.” Applied Energy 220 (2018): 1–12.
  23. van der Veen, Reinier AC, and Rudi A. Hakvoort. “The electricity balancing market: Exploring the design challenge.” Utilities Policy 43 (2016): 186–194.
  24. Lüth, Alexandra, et al. “Local electricity market designs for peer-to-peer trading: The role of battery flexibility.” Applied energy 229 (2018): 1233–1243.
  25. Kruizenga, Richard John. Put and call options: a theoretical and market analysis. Diss. Massachusetts Institute of Technology, 1956.
  26. Jeriha, J., et al. “National balancing and wholesale electricity markets structure and principles. CROSSBOW Consortium.” (2019).
  27. Correa-Henao, Gabriel Jaime, and José María Yusta-Loyo. “Representation of electric power systems by complex networks with applications to risk vulnerability assessment.” DYNA, 2015.
  28. Holmgren, Ake J. “Using Graph Models to Analyze the Vulnerability of Electric Power Networks.” Risk Analysis, vol. 26, no. 4, 2006.
  29. Albert, Réka, Hawoong Jeong, and Albert-László Barabási. “Error and attack tolerance of complex networks.” nature 406.6794 (2000): 378–382.
  30. MIT Energy Initiative. “Utility of the Future — An MIT Energy Initiative response to an industry in transition,” 2016.
  31. Singh, Harmanjit, and Richa Sharma. “Role of adjacency matrix & adjacency list in graph theory.” International Journal of Computers & Technology 3.1 (2012): 179–183.
  32. Cormen, Thomas H., et al. Introduction to Algorithms. Third ed., The MIT Press, 2009.
  33. Jargstorf, Johannes, Kris Kessels, and Ronnie Belmans. “Capacity-based grid fees for residential customers.” 2013 10th International Conference on the European Energy Market (EEM). IEEE, 2013.
  34. Temple, William G., and Richard TB Ma. “Monotonic marginal pricing: Demand response with price certainty.” ACM SIGMETRICS Performance Evaluation Review 41.3 (2014): 65–70.
  35. Namerikawa, Toru, and Yoshihiro Okawa. “Distributed Dynamic Pricing in Electricity Market with Information Privacy.” Economically Enabled Energy Management: Interplay Between Control Engineering and Economics. Springer Singapore, 2020. 213–244.
  36. Borowski, Piotr F. “Zonal and Nodal Models of energy market in European Union.” Energies 13.16 (2020): 4182.
  37. Pérez-Arriaga, Ignacio J., and Yves Smeers. “Guidelines on tariff setting.” Transport pricing of electricity networks. Springer, Boston, MA, 2003. 175–203.
  38. Wang, Yishu, et al. “Time-dependent graphs: Definitions, applications, and algorithms.” Data Science and Engineering 4.4 (2019): 352–366.
  39. Meeus, Leonardo, and Nouicer, Athir. The EU clean energy package. 2018.
  40. G. C. Okwuibe, M. Wadhwa, T. Brenner, P. Tzscheutschler and T. Hamacher, “Analysis of Key Performance Indicators for Local Electricity Markets’ Design Analyse des indicateurs clés de performance pour la conception des marchés locaux de l’électricité”, in IEEE Canadian Journal of Electrical and Computer Engineering, 2021.

--

--

Engineering open source software that simulates and operates grid-aware energy exchanges, creating local marketplaces that interconnect to form a smart grid

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Grid Singularity

Engineering open source software that simulates and operates grid-aware energy exchanges, creating local marketplaces that interconnect to form a smart grid