A General Sensitivity Analysis Approach for Demand Response Optimizations

It is well-known that demand response can improve the system efficiency as well as lower consumers' (prosumers') electricity bills. However, it is not clear how we can either qualitatively identify the prosumer with the most impact potential or quantitatively estimate each prosumer's contribution to the total social welfare improvement when additional resource capacity/flexibility is introduced to the system with demand response, such as allowing net-selling behavior. In this work, we build upon existing literature on the electricity market, which consists of price-taking prosumers each with various appliances, an electric utility company and a social welfare optimizing distribution system operator, to design a general sensitivity analysis approach (GSAA) that can estimate the potential of each consumer's contribution to the social welfare when given more resource capacity. GSAA is based on existence of an efficient competitive equilibrium, which we establish in the paper. When prosumers' utility functions are quadratic, GSAA can give closed forms characterization on social welfare improvement based on duality analysis. Furthermore, we extend GSAA to a general convex settings, i.e., utility functions with strong convexity and Lipschitz continuous gradient. Even without knowing the specific forms the utility functions, we can derive upper and lower bounds of the social welfare improvement potential of each prosumer, when extra resource is introduced. For both settings, several applications and numerical examples are provided: including extending AC comfort zone, ability of EV to discharge and net selling. The estimation results show that GSAA can be used to decide how to allocate potentially limited market resources in the most impactful way.Comment: 17 page

Identifying opportunities for developing CSP and PV-CSP hybrid projects under current tender conditions and market perspectives in MENA – benchmarking with PV-CCGT

Concentrating solar power (CSP) is one of the promising renewable energy technologies provided the fact that it is equipped with a cost-efficient storage system, thermal energy storage (TES). This solves the issue of intermittency of other renewable energy technologies and gives the advantage of achieving higher capacity factors and lower levelized costs of electricity (LCOE). This is the main reason why solar tower power plants (STPP) with molten salts and integrated TES are considered one of the most promising CSP technologies in the short term [1]. On the other hand, solar photovoltaic (PV) is a technology whose costs have been decreasing and are expected to continue doing so thus providing competitive LCOE values, but with relatively low capacity factors as electrical storage systems remain not cost-effective. Combining advantages and eliminating drawbacks of both technologies (CSP and PV), Hybridized PV-CSP power plants can be deemed as a competitive economic solution to offer firm output power when CSP is operated smartly so that its load is regulated in response to the PV output. Indeed previous works, have identified that it would allow achieving lower LCOEs than stand-alone CSP plants by means of allowing it to better utilize the solar field for storing energy during the daytime while PV is used [1]. On the fossil-based generation side, the gas turbine combined cycle (CCGT) occupies an outstanding position among power generation technologies. This is due to the fact that it is considered the most efficient fossil fuel-to-electricity converter, in addition to the maturity of such technology, high flexibility, and the generally low LCOE, which is largely dominated by fuel cost and varies depending on the natural gas price at a specific location. Obviously, the main drawback is the generated carbon emissions. In countries rich in natural gas resources and with vast potential for renewable energies implementation, such as the United Arab Emirates (UAE), abandoning a low LCOE technology with competitively low emissions – compared to coal or oil - and heading to costly pure renewable generation, seems like an aggressive plan. Therefore, hybridizing CCGT with renewable generation can be considered an attractive option for reducing emissions at reasonable costs. This is the case of the UAE with vast resources of both natural gas and solar energy. Previous work have shown the advantages of hybrid PV-CCGT and hybrid PV-CSP plants separately [1][2]. In this thesis, CSP and the two hybrid systems are compared on the basis of LCOE and CO2 emissions for a same firm-power capacity factor when considering a location in the UAE. The results are compared against each other to highlight the benefits of each technology from both environmental and economic standpoints and provide recommendations for future work in the field. The techno-economic analysis of CSP (STPP with TES), PV-CSP(STPP with TES) and PV-CCGT power plants have been performed by DYESOPT, an in-house tool developed in KTH, which runs techno-economic performance evaluation of power plants through multi-objective optimization for specific locations[1]. For this thesis, a convenient location in the UAE was chosen for simulating the performance of the plants. The UAE is endowed by the seventh-largest proven natural gas reserves and average to high global horizontal irradiation (GHI) and direct normal irradiation (DNI) values all year round, values considered to be lower than other countries in the MENA region due to its high aerosol concentrations and sand storms. The plants were designed to provide firm power in two cases, first as baseload, and second as intermediate load of 15 hours from 6:00 until 21:00. The hours of production were selected based on a typical average daily load profile. CSP and PV-CSP model previously developed by [3][1] were used. Ideally in the PV-CSP model, during daytime hours the PV generation is used for electricity production, covering the desired load, while CSP is used partly for electricity production and the rest for storing energy in the TES. Energy in the TES system is then used to supply firm power during both periods of low Irradiance and night hours or according to need. A PV-CCGT model has been developed which operates simultaneously, prioritizing the availability of PV while the CCGT fulfils the remaining requirement. There is a minimum loading for the CCGT plant which is determined by the minimum possible partial loading of the gas turbine restricted by the emission constraints. Accordingly, in some cases during operation PV is chosen to be curtailed due to this limitation. The main results of the techno-economic analysis are concluded in the comparative analysis of the 3 proposed power plant configurations, where the PV-CCGT plant is the most economic with minimum LCOE of 86 USD/MWh, yet, the least preferable option in terms of carbon emissions. CSP and PV-CSP provided higher LCOE, while the PV-CSP plant configuration met the same capacity factor with 11% reduction in LCOE, compared to CSP

Optimization by decomposition: A step from hierarchic to non-hierarchic systems

A new, non-hierarchic decomposition is formulated for system optimization that uses system analysis, system sensitivity analysis, temporary decoupled optimizations performed in the design subspaces corresponding to the disciplines and subsystems, and a coordination optimization concerned with the redistribution of responsibility for the constraint satisfaction and design trades among the disciplines and subsystems, and a coordination optimization concerned with the redistribution of responsibility for the constraint satisfaction and design trades among the disciplines and subsystems. The approach amounts to a variation of the well-known method of subspace optimization modified so that the analysis of the entire system is eliminated from the subspace optimization and the subspace optimizations may be performed concurrently

Optimization by decomposition: A step from hierarchic to non-hierarchic systems

A new, non-hierarchic decomposition is formulated for system optimization that uses system analysis, system sensitivity analysis, temporary decoupled optimizations performed in the design subspaces corresponding to the disciplines and subsystems, and a coordination optimization concerned with the redistribution of responsibility for the constraint satisfaction and design trades among the disciplines and subsystems. The approach amounts to a variation of the well-known method of subspace optimization modified so that the analysis of the entire system is eliminated from the subspace optimization and the subspace optimizations may be performed concurrently

The Canadian Debt-Strategy Model: An Overview of the Principal Elements

As part of managing a debt portfolio, debt managers face the challenging task of choosing a strategy that minimizes the cost of debt, subject to limitations on risk. The Bank of Canada provides debt-management analysis and advice to the Government of Canada to assist in this task, with the Canadian debt-strategy model being developed to help in this regard. The authors outline the main elements of the model, which include: cost and risk measures, inflation-linked debt, optimization techniques, the framework used to model the government’s funding requirement, the sensitivity of results to the choice of joint stochastic macroeconomic term-structure model, the effects of shocks to macroeconomic and term-structure variables and changes to their long-term values, and the relationship between issuance yield and issuance amount. Emphasis is placed on the degree to which changes to the formulation of model elements impact key results. The model is an important part of the decision-making process for the determination of the government’s debt strategy. However, it remains one of many tools that are available to debt managers and is to be used in conjunction with the judgment of an experienced debt manager.Debt management; Econometric and statistical methods; Financial markets; Fiscal policy

Approximated Computation of Belief Functions for Robust Design Optimization

This paper presents some ideas to reduce the computational cost of evidence-based robust design optimization. Evidence Theory crystallizes both the aleatory and epistemic uncertainties in the design parameters, providing two quantitative measures, Belief and Plausibility, of the credibility of the computed value of the design budgets. The paper proposes some techniques to compute an approximation of Belief and Plausibility at a cost that is a fraction of the one required for an accurate calculation of the two values. Some simple test cases will show how the proposed techniques scale with the dimension of the problem. Finally a simple example of spacecraft system design is presented.Comment: AIAA-2012-1932 14th AIAA Non-Deterministic Approaches Conference. 23-26 April 2012 Sheraton Waikiki, Honolulu, Hawai

Modeling Storage and Demand Management in Electricity Distribution Grids

Storage devices and demand control may constitute beneficial tools to optimize electricity generation with a large share of intermittent resources through inter-temporal substitution of load. We quantify the related cost reductions in a simulation model of a simplified stylized medium-voltage grid (10kV) under uncertain demand and wind output. Benders Decomposition Method is applied to create a two-stage stochastic program. The model informs an optimal investment sizing decision as regards specific 'smart grid' applications such as storage facilities and meters enabling load control. Model results indicate that central storage facilities are a more promising option for generation cost reductions as compared to demand management. Grid extensions are not appropriate in any of our scenarios. A sensitivity analysis is applied with respect to the market penetration of uncoordinated Plug-In Electric Vehicles which are found to strongly encourage investment into load control equipment for `smart` charging and slightly improve the case for central storage devices.Storage, demand management, stochastic optimization, Benders Decomposition