{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/52026"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/52026","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"A Game-theoretic Framework to Investigate Conditions for Cooperation between Wind Power Producers and Energy Storage Operators","abstract":"Game theory has its applications in various domains, but has only recently been applied to study open problems in smart microgrids. A simple microgrid system with a small wind farm, a storage facility and an aggregate load entity is studied here using a non-cooperative game-theoretic framework. The framework developed is used to study the behavior of rational market participants (players), namely wind power producer and energy storage. The framework is implemented to find the existence of any Nash equilibria and see if cooperation is a natural outcome of the game. If cooperation is not self-enforcing then usefulness of the framework to find the conditions for cooperation is presented. It must be noted that cooperation is not automatically guaranteed as the payoff of the energy storage operator is dependent on the strategy employed by the wind power producer. Similarly, the payoff for the wind power producer is highly intertwined with the strategy employed by the energy storage operator. Historical weather and market data is used to calculate expected payoffs for each possible combination of strategies. The results are presented in the form of payoff matrices and the best response algorithm and/or elimination of dominated strategies is used to find the Nash equilibrium. Sensitivity of the Nash equilibrium to various storage parameters like storage size, charging/discharging limits, charging/discharging efficiency, and other market parameters like energy imbalance penalties, efficiency of up/down regulation, and electricity market prices is studied and necessary conditions for cooperation are presented.","abstract_html":"Game theory has its applications in various domains, but has only recently been applied to study open problems in smart microgrids. A simple microgrid system with a small wind farm, a storage facility and an aggregate load entity is studied here using a non-cooperative game-theoretic framework. The framework developed is used to study the behavior of rational market participants (players), namely wind power producer and energy storage. The framework is implemented to find the existence of any Nash equilibria and see if cooperation is a natural outcome of the game. If cooperation is not self-enforcing then usefulness of the framework to find the conditions for cooperation is presented. It must be noted that cooperation is not automatically guaranteed as the payoff of the energy storage operator is dependent on the strategy employed by the wind power producer. Similarly, the payoff for the wind power producer is highly intertwined with the strategy employed by the energy storage operator. Historical weather and market data is used to calculate expected payoffs for each possible combination of strategies. The results are presented in the form of payoff matrices and the best response algorithm and/or elimination of dominated strategies is used to find the Nash equilibrium. Sensitivity of the Nash equilibrium to various storage parameters like storage size, charging/discharging limits, charging/discharging efficiency, and other market parameters like energy imbalance penalties, efficiency of up/down regulation, and electricity market prices is studied and necessary conditions for cooperation are presented.","abstract_has_math":false,"creators":["Bhela, Siddharth"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Electrical Engineering","degree_department":"Electrical and Computer Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Tam, Kwa Sur"],"committee_members":["Saad, Walid","Centeno, Virgilio A."],"year":2015,"date_issued":"2015-05-05","date_published":"2015-05-05","updated_at":"2026-07-22T22:20:00Z","subjects":["Game Theory","Wind Power","Energy Storage","Electricity Markets","Smart Microgrids","Optimization"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:5423"],"render_values":[{"text":"vt_gsexam:5423","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/52026","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Tam, Kwa Sur"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Saad, Walid","Centeno, Virgilio A."]},{"key":"dc:contributor.department","label":"Department","values":["Electrical and Computer Engineering"]},{"key":"dc:creator","label":"Author","values":["Bhela, Siddharth"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2015-05-06T08:01:04Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2015-05-06T08:01:04Z"]},{"key":"dc:date.issued","label":"Date","values":["2015-05-05"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Game Theory","Wind Power","Energy Storage","Electricity Markets","Smart Microgrids","Optimization"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:5423"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/52026"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Game theory has its applications in various domains, but has only recently been applied to study open problems in smart microgrids. A simple microgrid system with a small wind farm, a storage facility and an aggregate load entity is studied here using a non-cooperative game-theoretic framework. The framework developed is used to study the behavior of rational market participants (players), namely wind power producer and energy storage. The framework is implemented to find the existence of any Nash equilibria and see if cooperation is a natural outcome of the game. If cooperation is not self-enforcing then usefulness of the framework to find the conditions for cooperation is presented. It must be noted that cooperation is not automatically guaranteed as the payoff of the energy storage operator is dependent on the strategy employed by the wind power producer. Similarly, the payoff for the wind power producer is highly intertwined with the strategy employed by the energy storage operator. Historical weather and market data is used to calculate expected payoffs for each possible combination of strategies. The results are presented in the form of payoff matrices and the best response algorithm and/or elimination of dominated strategies is used to find the Nash equilibrium. Sensitivity of the Nash equilibrium to various storage parameters like storage size, charging/discharging limits, charging/discharging efficiency, and other market parameters like energy imbalance penalties, efficiency of up/down regulation, and electricity market prices is studied and necessary conditions for cooperation are presented."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["A Game-theoretic Framework to Investigate Conditions for Cooperation between Wind Power Producers and Energy Storage Operators"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Tam, Kwa Sur"],"dc:contributor.committeemember":["Saad, Walid","Centeno, Virgilio A."],"dc:contributor.department":["Electrical and Computer Engineering"],"dc:creator":["Bhela, Siddharth"],"dc:date.accessioned":["2015-05-06T08:01:04Z"],"dc:date.available":["2015-05-06T08:01:04Z"],"dc:date.issued":["2015-05-05"],"dc:description.abstract":["Game theory has its applications in various domains, but has only recently been applied to study open problems in smart microgrids. A simple microgrid system with a small wind farm, a storage facility and an aggregate load entity is studied here using a non-cooperative game-theoretic framework. The framework developed is used to study the behavior of rational market participants (players), namely wind power producer and energy storage. The framework is implemented to find the existence of any Nash equilibria and see if cooperation is a natural outcome of the game. If cooperation is not self-enforcing then usefulness of the framework to find the conditions for cooperation is presented. It must be noted that cooperation is not automatically guaranteed as the payoff of the energy storage operator is dependent on the strategy employed by the wind power producer. Similarly, the payoff for the wind power producer is highly intertwined with the strategy employed by the energy storage operator. Historical weather and market data is used to calculate expected payoffs for each possible combination of strategies. The results are presented in the form of payoff matrices and the best response algorithm and/or elimination of dominated strategies is used to find the Nash equilibrium. Sensitivity of the Nash equilibrium to various storage parameters like storage size, charging/discharging limits, charging/discharging efficiency, and other market parameters like energy imbalance penalties, efficiency of up/down regulation, and electricity market prices is studied and necessary conditions for cooperation are presented."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:5423"],"dc:identifier.uri":["http://hdl.handle.net/10919/52026"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Game Theory","Wind Power","Energy Storage","Electricity Markets","Smart Microgrids","Optimization"],"dc:title":["A Game-theoretic Framework to Investigate Conditions for Cooperation between Wind Power Producers and Energy Storage Operators"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:00Z"}