The Development of a Risk Assessment Modeling for the Power System of Kazakhstan

Zhakiyeva, Svetlana; Gabbassov, M. B.; Akhmetbekov, Yerbol; Akybayeva, Gulvira; Zhakiyev, Nurkhat

doi:10.1109/sist50301.2021.9465892

Cited by 2 publications

(6 citation statements)

References 14 publications

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“…it is seen that Bus 15 is MVP and has 100% share of the profit, Bus 31 is LVP and has a 25% share, and other players are TVP with a share of 50%. Therefore, a 15 = 1, a 31 = 0.25, a i = 0.5; i / ∈ (15,31) are inserted into Equation (36). ∈ (15,31).…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, a 15 = 1, a 31 = 0.25, a i = 0.5; i / ∈ (15,31) are inserted into Equation (36). ∈ (15,31). The proposed TE framework is a feasible solution to implement within existing radial networks with the support of the DSO.…”

Section: Resultsmentioning

confidence: 99%

“…The TE trading agents participate in continuous double auctions conducted by TEMA and use RBB as a bidding strategy. This strategy allows agents to place bids according to degree of risk [31]. In this strategy, player agents calculate the risk-return trade-off.…”

Section: Rbb-risk-based Biddingmentioning

confidence: 99%

“…where TL l is total cost variation with π + and without π − line limits in line l, where π are Lagrangian multipliers of line limit constraints in (31).…”

Section: Dlmp-based Market Mechanismmentioning

confidence: 99%

“…Flow Chart for entire framework. The share of DSO and TE service providers were chosen as 80% and 20% of the remaining schedule: b 15 = c 15 = 0, b 31 = 0.6, c 31 = 0.15, b i = 0.4, c i = 0.1; i /∈(15,31). The proposed TE framework is a feasible solution to implement within existing radial networks with the support of the DSO.…”

mentioning

confidence: 99%

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Distribution Locational Marginal Price Based Transactive Energy Management in Distribution Systems with Smart Prosumers—A Multi-Agent Approach

et al. 2022

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This work proposes a distribution locational marginal price (DLMP)-based transactive energy (TE) framework for distribution systems with enthusiastic or smart prosumers. The framework uses a multi-agent system (MAS) as the basis on which the proposed TE model, i.e., distribution locational marginal price (DLMP) based TE management system (DTEMS), is implemented. DTEMS uses a novel metric known as the nodal earning component, which is determined by the optimal power flow (OPF) based smart auction mechanism, to schedule the TE transactions optimally among the stakeholders by alleviating the congestion in the distribution system. Based on the individual contributions to the congestion relief, DTEMS ranks the prosumers and loads as most valuable players (MVP) and assigns the energy trading price according to the category of the player. The effectiveness of the proposed TE model is verified by simulating the proposed DTEMS for a modified 33 bus radial distribution system fed with various plug-able energy resources, prosumers, and microgrids.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Rbb-risk-based Biddingmentioning

confidence: 99%

“…where TL l is total cost variation with π + and without π − line limits in line l, where π are Lagrangian multipliers of line limit constraints in (31).…”

Section: Dlmp-based Market Mechanismmentioning

confidence: 99%

mentioning

confidence: 99%

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Distribution Locational Marginal Price Based Transactive Energy Management in Distribution Systems with Smart Prosumers—A Multi-Agent Approach

et al. 2022

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Optimization Modelling of the Decarbonization Scenario of the Total Energy System of Kazakhstan until 2060

et al. 2023

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This research article provides a comprehensive scenario analysis of key structural changes in Kazakhstan’s fuel and energy complex subsectors until 2060, focusing on decarbonization efforts. The background places the issue of decarbonization in a broader context, considering the country’s vast size and sparse population. The study’s purpose involves analyzing the development of the climate agenda by comparing two scenarios: a “reference” scenario without decarbonization measures and a carbon neutrality scenario until 2060 (CN2060). A mathematical technical-economic model based on the TIMES paradigm (The Integrated MARKAL-EFOM System) serves as the method to optimize and simulate Kazakhstan’s energy system. The main findings reveal sets of policies, standards, and legislative, economic, and political decisions that are required to achieve CN2060. Additionally, the integration of a low-carbon policy, sectoral and cross-cutting approaches, the impact of the coronavirus crisis, the Russia-Ukraine conflict, and energy security issues receive a discussion. The article concludes with projected shares of generation and investment in renewable energy sources (RES) necessary for attaining CN2060. This work offers novel insights into challenges and opportunities for Kazakhstan’s transition to a low-carbon economy.

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The Development of a Risk Assessment Modeling for the Power System of Kazakhstan

Cited by 2 publications

References 14 publications

Distribution Locational Marginal Price Based Transactive Energy Management in Distribution Systems with Smart Prosumers—A Multi-Agent Approach

Distribution Locational Marginal Price Based Transactive Energy Management in Distribution Systems with Smart Prosumers—A Multi-Agent Approach

Optimization Modelling of the Decarbonization Scenario of the Total Energy System of Kazakhstan until 2060

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