Cooperative Game-theoretic Approach to Load Balancing in Smart Grids with Community Energy Storage

In this paper, we propose a model for households to share energy from community energy storage (CES) such that both households and utility company benefit from CES. In addition to providing a range of ancillary grid services, CES can also be used for demand side management, to shave peaks and fill valleys in system load. We introduce a method stemming from consumer theory and cooperative game theory that uses CES to balance the load of an entire locality and manage household energy allocations respectively. Load balancing is derived as a geometric programming problem. Each household’s contribution to overall non-uniformity of the load profile is modeled using a characteristic function and Shapley values are used to allocate the amount and price of surplus energy stored in CES. The proposed method is able to perfectly balance the load while also making sure that each household is guaranteed a reduction in energy costs.Peer reviewe

Local flexibility market design for aggregators providing multiple flexibility services at distribution network level

This paper presents a general description of local flexibility markets as a market-based management mechanism for aggregators. The high penetration of distributed energy resources introduces new flexibility services like prosumer or community self-balancing, congestion management and time-of-use optimization. This work is focused on the flexibility framework to enable multiple participants to compete for selling or buying flexibility. In this framework, the aggregator acts as a local market operator and supervises flexibility transactions of the local energy community. Local market participation is voluntary. Potential flexibility stakeholders are the distribution system operator, the balance responsible party and end-users themselves. Flexibility is sold by means of loads, generators, storage units and electric vehicles. Finally, this paper presents needed interactions between all local market stakeholders, the corresponding inputs and outputs of local market operation algorithms from participants and a case study to highlight the application of the local flexibility market in three scenarios. The local market framework could postpone grid upgrades, reduce energy costs and increase distribution grids’ hosting capacity.Postprint (published version

Flexibility Characterization, Aggregation, and Market Design Trends with a High Share of Renewables: a Review

Purpose of Review Balancing a large share of solar and wind power generation in the power system will require a well synchronized coordination of all possible flexibility sources. This entails developing market designs that incentivize flexibility providers, and define new flexibility products. To this end, the paper reviews latest trends in the characterization of flexibility by understanding its dimensions in terms of time, spatiality, resource type, and associated risks. Also, as aggregators have emerged as important actors to deliver, and to reward end-user flexibility, the paper reviews latest trends in the topic. Recent Findings The review reports latest trends and discussions on power system flexibility and their relations to market design. The current academic literature indicates that there are open question and limited research on how to reward shortterm flexibility while considering its long-term economic viability. Demand-side flexibility through aggregation holds great potential to integrate renewables. Summary Research in power system flexibility has to put effort on analysing new time-structures of electricity markets and define new marketplaces that consider the integration of new flexibility products, actors (e.g. aggregators, end-users), and mechanisms (e.g. TSO-DSO coordination).Flexibility Characterization, Aggregation, and Market Design Trends with a High Share of Renewables: a ReviewpublishedVersio

Swarm electrification: Harnessing surplus energy in off-grid solar home systems for universal electricity access

peer reviewedAchieving universal access to electricity by 2030, as set out by the Sustainable Development Goals, presents a significant challenge given the current rate of progress. A recent promising concept is swarm electrification. Its central idea is the peer-to-peer energy sharing of surplus energy in solar home systems (SHSs) to connect additional neighbors and grow a bottom-up grid. This paper studies the surplus energy in SHSs and its underlying influencing factors as a basis for swarm electrification. An open-source multi-model-based techno-economic analysis of off-grid SHS including surplus energy as a value is presented. Three distinct household types from the tier 3 category in the Multi-tier framework are compared based on their unique ratios of peak-to-average demand and percentage of load consumption during sun hours. A statistical analysis of surplus energy for each household type is presented and energy sharing with additional households at tier 1–2 is simulated. Two economic analysis methods, including surplus energy, are presented and compared: single-objective cost minimization and multi-objective compromise programming. The study finds that a low ratio of demand during sun hours leads to higher surplus energy volumes, while a peak-to-average ratio alone cannot give such indications. Both economic methods suggest that optimizing the SHS design for tier 3 households involves a slight increase in solar power capacity when considering the expected revenue from selling surplus energy to 2–3 households in tiers 1–2. The total cost for the tier 3 households are reduced by 40%−64%, additionally to decreasing their own lost load by 4%−7%, and reducing the up-front cost to get electricity access for the tier 1–2 households by 50% compared to purchasing their own full SHS

Modeling Cooperative Behavior in Smart Grid and Cognitive Radio Systems

Cooperation is an instinctive and evolutionary trait in humans that results in mutual benefits for all persons involved irrespective of whether we live in a hunter-gatherer society or a digital economy. Similarly, independent rational players in complex multi-user intelligent systems such as smart grids (SG) and cognitive radios (CR) also benefit from cooperative behavior. Quantifying and sharing the benefits of cooperation amongst all players in a fair and stable manner is a non-trivial problem of great interest. The focus of this thesis is on modeling cooperative interactions for achieving demand side management (DSM) in SGs and dynamic spectrum access (DSA) in CRs using cooperative game theory. A critical challenge in electricity delivery is that energy supply does not follow consumer demand with typical peaks and valleys at different time periods. The integration of distributed renewable energy sources (RES) and information technology in SGs allows for overcoming this mismatch by means of DSM. DSM involves modification of consumer energy demand through price-based demand response (DR) models or by means of local energy markets. Highly efficient DR algorithms are proposed for cost minimization and load balancing for households with energy storage systems (ESS) under time of use pricing. Using concepts from consumer theory and intertemporal trading, cost minimization is formulated as a linear programming problem, while load balancing is formulated as a geometric programming problem. The proposed load balancing method performs very well with peak to average ratio (PAR) values close to 1. Local energy trading is modeled separately as an exchange economy for households with ESS and as a production economy for minigrids with hybrid RES. Due to continuous increase in spectrum demand, certain bands face severe scarcity and yet, a large portion of spectrum is often under-utilized across time and space. Apparent scarcity in spectrum arises from rigid and inefficient spectrum allocation policy rather than actual physical shortage of spectrum. DSA facilitates flexible spectrum usage by providing the capability for unlicensed secondary users (SUs) to sense the spectrum and opportunistically share unused licensed bands without causing harmful interference to licensed primary users (PUs). A cooperative game for jointly modeling spectrum sensing and sharing problem in CRs is proposed, whereby idle spectrum is allocated to SUs based on their sensing performance. The characteristic function that forms the basis for fair division of benefits of cooperation among SUs is derived. The proposed cooperative game for joint spectrum sensing and sharing results in the formation of a grand coalition and provides the best balance between fairness, cooperation and performance in terms of data rate achieved by SUs

Dynamic Capabilities in Electrical Energy Digitalization: A Case from the Norwegian Ecosystem

This paper aims to identify the dynamic capabilities required for electrical energy service providers to transform toward a digital and platform-based business models in the context of the current energy transition. The paper contributes to two fields: Information systems in the domain of platforms ecosystems and digital services innovation through the usage of dynamic capabilities theoretical lens and the field of energy informatics in the domain of digital business models and service innovation. Through the case study approach we investigate the case of Norwegian electrical energy provider TrønderEnergi and how the company is moving toward a fully digital business model and how the company build the dynamic capabilities required for the digitalization era. Through semi-structured interviews, the study managed to identify several activities related to each capability and then classified these activities under three main activities, which are: sensing, seizing, and transforming, and then classified them into sub-capabilities and identified activities related to each sup capability. The paper concludes with managerial implications for practitioners and initiates an empirical extension for the dynamic capabilities theoretical lens

Dynamic Capabilities in Electrical Energy Digitalization: A Case from the Norwegian Ecosystem

This paper aims to identify the dynamic capabilities required for electrical energy service providers to transform toward a digital and platform-based business models in the context of the current energy transition. The paper contributes to two fields: Information systems in the domain of platforms ecosystems and digital services innovation through the usage of dynamic capabilities theoretical lens and the field of energy informatics in the domain of digital business models and service innovation. Through the case study approach we investigate the case of Norwegian electrical energy provider TrønderEnergi and how the company is moving toward a fully digital business model and how the company build the dynamic capabilities required for the digitalization era. Through semi-structured interviews, the study managed to identify several activities related to each capability and then classified these activities under three main activities, which are: sensing, seizing, and transforming, and then classified them into sub-capabilities and identified activities related to each sup capability. The paper concludes with managerial implications for practitioners and initiates an empirical extension for the dynamic capabilities theoretical lens