Unit Commitment Using Embedded Systems

AbstractUnit commitment problem helps in deciding which electricity generation unit should be running in each period so as to satisfy a predictably varying demand for electricity. Unit Commitment enables uninterruptible power to be delivered to consumers using the principle of minimum operating cost. In this paper a laboratory prototype for unit commitment is developed using embedded systems. In this work, the unit commitment problem is solved using dynamic programming approach. The generators are switched ON and OFF on a priority basis to minimize the total operating cost of the generating units. An Embedded Development Kit(EDK) is used for the prototype which supports micro framework technology. The laboratory prototype is tested for various combinations of generating units

Risk‐return optimised energy asset allocation in transmission‐distribution system using tangency portfolio and Black–Litterman model

Abstract The application of Markowitz and tangency portfolio and Black–Litterman models is extended by the authors to energy portfolio selection in transmission‐distribution environments with high penetration of renewable energy. As Transmission System Operator (TSO) and Distribution System Operator (DSO) contextually take mutualistic or conflicting positions in their portfolio selection process, their risk‐return interactions and behaviours depend on their subjective views on generation and operation. Here, the financial portfolio allocation tool Black–Litterman Model is adapted to incorporate subjective views of the operators to arrive at more intuitive portfolios. The best portfolios are searched within the acceptable risk‐return search space of each operator defined by their Markowitz efficient frontiers (EF), for Pareto‐optimising their profits. The tangency portfolio approach, which is generally used to determine the portfolio of risky and risk‐free assets in finance, is used here to determine the portfolio of renewable (energy‐risky) and fuel‐based sources (energy‐risk‐free). The proposed methodology is adopted in an HV–MV interconnected test system operated by one TSO and two DSOs, having wind, solar, coal, gas and nuclear generation technologies. It is observed that completely customisable portfolios can be constructed for TSO and DSO based on their inherent financial and energy risk‐return behaviours and posterior views

Tail risk adjusted clean energy portfolios in P2P transactive markets using Rachev ratio

Abstract Renewable energy sources (RES) and electric vehicles (EVs) pose ‘energy‐risk’ in peer energy commitments due to their temporal and spatial uncertainties. Thus, optimistic commitments in the peer‐to‐peer transactive energy market (P2P TEM) are improbable. This paper proposes a two‐stage master–slave portfolio optimization approach for combining energy‐risk of RES and EVs, and welfare‐risk of peers, in building clean energy portfolios. The master portfolio (MP) refers to the shares of renewable and EVs in P2P market settlement, whereas the slave portfolio (SP) gives the wind‐solar mix within renewables. Here, Rachev Ratio (RR), an index used in financial portfolio selection for tail‐risk management, is adapted and combined with Markowitz Efficient Frontier (EF) to find the optimal slave portfolio. Both the extreme tails are optimized, encouraging energy outputs far above forecast and discouraging those far below forecast. The master portfolio is obtained by maximizing the sum of the average welfare of the peers at the best (right) and worst (left) tails of the welfare distribution curve using Stochastic Weight Trade‐off Particle Swarm Optimization (SWT‐PSO). The proposed portfolio selection approach is better in terms of increased expected energy output, improved utilization of RES and EVs, and better collective peer welfare

Multi‐stage energy‐risk adjustments using practical byzantine fault tolerance consensus for blockchain‐powered peer‐to‐peer transactive markets

Abstract The energy risk associated with distributed energy resources (DERs) is inevitable in Peer‐to‐Peer (P2P) transactive energy markets owing to mismatches between energy commitments and metered measurements. However, adjusting these possible mismatches by progressive revision of the energy commitments in the rolling time horizon mitigates the energy risk, and thereby mitigates the financial risk for prosumers. In this study, the conditional value at risk (CVaR) is used to estimate the risk value for each prosumer. The energy offers that are riskier than CVaR‐based threshold values are reduced in an “adjustment bid”. A new pricing mechanism for these adjustment bids is introduced, which varies with historical deviations of a prosumer from energy commitments. This market framework and pricing mechanism are simulated through a blockchain network hosted on a Python Django server using the practical Byzantine fault tolerance consensus algorithm to guarantee network immutability and data privacy. Efforts to mitigate such mismatches between ex‐ante and ex‐post energy values incentivise risk‐aware participation in P2P markets. In addition, the welfare of both prosumers and consumers improves with their participation in the proposed market framework. Furthermore, implementing a network using blockchain technology guarantees the privacy of bidding data and provides a secure transaction platform

Realistic energy commitments in peer-to-peer transactive market with risk adjusted prosumer welfare maximization

202205 bchyNot applicableOthersSPARC, Ministry of Human Resources and Development, Government of IndiaPublished24 month

Policy assistance for adoption of residential solar PV in India: A stakeholder-centric approach for welfare optimization

This paper presents a comprehensive analysis of the consumer-centric business model for rooftop solar PV installations in India. We explore areas where potential policy interventions may be introduced to improve collective stakeholder benefits and incentivize more domestic consumers to adopt rooftop solar power generation in their premises. The proposed policy framework optimizes Feed-in Tariff (FiT) rates, PV capacities and Average Billing Rates (ABRs) towards maximizing stakeholder benefits. The stakeholders considered are the consumers/prosumers and the utility. Case studies with three residential prosumers of different demand and generation profiles are presented. The models for utility profit and prosumer savings are developed, and a multi-objective problem is formulated with FiT, generation capacity (as a function of demand) and ABR as decision variables. The pareto-optimal front is identified for prosumer and utility benefits and suitable points with reasonable tradeoffs are selected based on sensitivity analysis of the impact on collective welfare. The suitability of prevailing tariff and FiT rates of two Indian utilities namely, MSEDCL and TATA POWER, Delhi are studied, and their impact on prosumer savings and utility profits is brought out. The workflow to fix tariff, FiT and local PV capacities in active residential distribution systems is devised, providing the policymakers an effective decision-making tool