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Achieving the low-carbon transition of power systems requires the coordination of renewable energy generation and other emerging electricity technologies. Their technical indicators are changing rapidly since these technologies are in the initial stages of development. Determining the technical and policy targets for achieving the low-carbon transition is critical for regulators and power system planners. This paper defines this problem as a backcasting problem, whose concept is derived from environmental science research. We introduce it into the power system planning field and innovatively model it as a bi-level optimization problem. According to the characteristics of the backcasting problem, an improved barrier method is proposed to solve it efficiently. The effectiveness of the proposed model and algorithm are verified through case studies on a modified Garver’s 6-bus system and a realistic Northwest China power system, and they are compared with state-of-the-art solution techniques. The results of the backcasting problem provide a new perspective on the economic impacts of the low-carbon transition and are helpful in energy policy formulation for power systems. [ABSTRACT FROM AUTHOR]

Regarding the continuing increase of anthropogenic carbon emissions in the power system with growing energy consumption, researchers have focused on managing the demand side to achieve low-carbon transition. However, such a low-carbon transition cannot be achieved effectively without the proper incentive scheme. Moreover, it is desired to improve the computation quality of carbon tracing tools and extend their applications by state-of-art techniques. To fulfil these existing research gaps, this paper proposes a low-carbon optimal scheduling model with demand response (DR) based carbon intensity control. It aims to reduce the dependence of customers on energy sources with high carbon intensities while decreasing reasonable energy consumption. Likewise, a data-driven approach conducted with the Bayesian interfere regression is proposed to carry out the carbon emission flow (CEF) model to cope with the drawbacks of the conventional emission calculation. Moreover, the storage emission and relevant dynamic impacts to the system are fully considered. The proposed data-driven approach is tested on a series of IEEE systems with different sizes, its high accuracy and efficiency are proved by the simulation results. Further, simulation results indicate that the proposed scheduling model can effectively achieve carbon emission mitigation. [ABSTRACT FROM AUTHOR]

To contribute to a smart grid, the unit commitment and dispatch (UCD) is a very important problem to be revisited in the future microgrid environment. This paper studies the UCD problem by taking account of both traditional thermal units and distributed generation units while considering demand response and carbon emissions trading. First, the UCD problem is reformulated as an optimal switching problem of a hybrid system. The reformulation allows for convenient handling of time-dependent start-up cost by dynamic programming. Then, we propose a closed-loop hierarchical operation (CLHO) algorithm, which produces the optimal schedule with a closed-loop feature. Theoretical analysis shows the equivalence of the reformulation and the effectiveness of the CLHO algorithm. Finally, the closed-loop feature and the effectiveness of the proposed algorithm are further verified by simulations. The simulations also show that low emission allowance and high emissions trading price can reduce the total carbon emissions. [ABSTRACT FROM AUTHOR]

The acceleration of distributed energy resources and carbon pricing policies have compelled utilities to act and to prioritize carbon-constrained infrastructure augmentation in their capital programs. To implement various carbon emission reduction policies, power system transmission planning has become more challenging. The existing energy system will face massive retirement of coal-fired power plants (CFPPs), large scale integration of renewable energy and network expansion. In this paper, an electricity network planning and transformation roadmap, which has two milestones, is put forward. In the first stage, a mathematical model is proposed based on the average cost of carbon emission reduction to realize the cooperation of CFPPs retirement and renewable energy investment. It can help the network carry out the transition from a fossil-fuel dominated system to a low-carbon oriented system. Because of the promising prospect of power-to-gas (P2G) technology, in the second milestone, a method based on carbon emission flow (CEF) is employed to help the power-to-gas stations (P2GSes) to select the construction site and capacity. The gas network constraints are modeled to guarantee that P2GSes can work smoothly without energy flow congestion in both electricity and gas networks. According to the simulation results in case studies, our method can reach the emission reduction target more economically and effectively, and the P2GSes can produce and absorb clean energy. [ABSTRACT FROM AUTHOR]

The increasing penetration of electric vehicles (EVs) results in growing interdependency between power and transportation networks. This paper proposes a hybrid economic-emission dispatch model to coordinate the operation of both networks toward social optima. Specifically, carbon emission from gasoline vehicles (GVs) is quantified by the macroscopic emission model, while the emission from EVs is indirectly characterized by that emitted from fossil power plants. To enhance regulation efficiency and improve social welfare, a differentiated pricing scheme is proposed to independently regulate GV traffic flow, EV traffic flow, and EV charging flow. Logarithmic transformation, combined with an accuracy-aware adaptive piecewise linearization (AAPWL) approximation method, is developed to reformulate the original nonlinear model into a tractable mixed-integer quadratic constrained programming. Numerical results from two test systems demonstrate the relationship between economic dispatch and emission dispatch. Meanwhile, the effectiveness and superiority of the proposed pricing scheme and the AAPWL method are validated. [ABSTRACT FROM AUTHOR]

Most existing carbon emission management strategies only control the total carbon emission without focusing on both the regional carbon emission and the stochastic properties of the system. Correlated regional loads and unpredictable renewable energies in the power system make regional carbon emission management (RCEM) increasingly challenging and necessary. A complex multi-objective RCEM model based on probabilistic power flow (PPF) considering correlated variables is contributed in this paper. The three objective functions to be minimized are 1) the cost of electricity generated, 2) the total carbon emission, and 3) the carbon emission difference among regions which reflects the regional carbon emission imbalance from the supply side. A new clonal selection algorithm (CSA) coupled with a fuzzy satisfying decision method and an extended 2 m +1 point estimate method (PEM) is proposed to solve this multi-objective RCEM model. The proposed method is illustrated through IEEE 30-bus, IEEE 118-bus and simplified Shanghai case studies. The proposed model can help reduce the total carbon emission, control regional carbon emission, prevent probabilistic congested lines from overloading, and choose the most suitable region for wind farms (WFs). [ABSTRACT FROM PUBLISHER]

Carbon financing policies such as emission trading have been used to assist in emission mitigation worldwide. As energy end-users/consumers are the underlying driver of emissions, it would be difficult to effectively mitigate carbon emissions by creating an emission trading market without active end-users’ involvement. In electricity markets, demand side management (DSM) in the smart grid can manage demands in response to power supply conditions and influence end-users to contribute to improving both network efficiency and economic efficiency. However, it is a relatively new topic to study the environmental benefits of DSM. This paper proposes a two-stage scheduling model to comprehensively investigate the environmental benefits of consumers participating in both electricity and carbon emission trading markets through active DSM. A developed zero sum gains-data envelopment analysis (ZSG-DEA) model based multi-criteria allocation scheme for emission allocation is employed. Meanwhile, the carbon emission flow model (CEF) is applied to track the “virtual” carbon flow accompanying power flow. According to case studies on the IEEE 24-bus system and IEEE 118-bus system, the proposed model can effectively achieve carbon emission mitigation and provide consumers extra environmental benefits in some scenarios. This model can be an important guide for governments to establish emission trading schemes. [ABSTRACT FROM AUTHOR]

This paper discusses the modeling and analysis of a carbon capture technology-based resource scheduling/unit commitment (UC) methodology in carbon/emission-constrained environment. In carbon markets, the overall generation from conventional/fossil-fueled thermal plants is constrained over the total cost which is the sum of generation, capture cost/emission avoidance cost. Therefore, in this paper, generalized/uniform performance indices affecting the UC schedule are derived. Also, a commitment/scheduling methodology based on capture and fuel cost is devised rather than the existing penalty cost methodology as in the modernized markets everything was dealt in monetary value. Furthermore, the impact of type of resource used, i.e., coal rank is also considered to evaluate the sensitivity of scheduling decisions and financial influence of carbon capture technology. Along with performance indices, correction factors are also proposed to justify the effect of resource/coal rank in the process of optimal generation allocation. The effect of correction factors and type of coal is observed to be predominant at lower capture efficiencies compared to higher capture efficiency. The effectiveness of the proposed method over the penalty method is reflected in reduced generation cost and emission avoidance cost when compared to the penalty-based methodology. [ABSTRACT FROM AUTHOR]

Multi-energy systems can provide a constant level of service to end-use energy demands, while deriving delivered energy from a variety of primary/secondary energy sources. This fuel-switching capability can be used to reduce operating expenses, reduce environmental impacts, improve flexibility to accommodate renewable energy, and improve reliability. This paper presents four frameworks for incentivizing energy hub equipment investments for low-carbon operation targets. These frameworks vary in the measures taken to achieve low-carbon operation (explicit constraint versus carbon pricing) and in the relationship between the hub builder and operator (cooperative versus uncoordinated). The underlying energy hub model upon which these frameworks are built is an enhanced greenfield model, introducing “energy buses” to reduce dimensionality. A case study is conducted for a campus being designed in Beijing, and results from each framework are compared to illustrate their relative costs. When the operator cannot be trusted to cooperate in controlling emissions, the system must be “overbuilt” with more expensive equipment to ensure emissions target are met. A taxation-based approach increases overall costs at moderate emissions targets, but this effect decreases at aggressive targets. This paper also compares the cost of less efficient institutional frameworks with the most efficient approach, i.e., cooperation between builder and operator with constraints on emissions. [ABSTRACT FROM AUTHOR]

Managed charging of electric vehicle (EV) loads has the potential to use renewable energy more effectively, shave peak demand, and fill demand valleys while serving transportation needs. However, the potential value to the grid from managed charging has not been fully quantified. This paper adopts the tools used in the National Renewable Energy Laboratory's California Low Carbon Grid Study to quantify value to the grid from managed charging by using three levels of managed loads for 13 TW·h of annual load from three million EVs in a 2030 California grid scenario. Simulation results show that management of the EV fleet's aggregate load from unmanaged to 100% managed results in savings between $210 million and $660 million annually in generation system costs, depending on grid conditions. The simulation results also suggest that targeted EV supply equipment (EVSE) deployments at workplaces and other mid-day parking locations will be needed to support managed charging in a high-renewables California and enable the identified value to the grid. Although the value of generation to the grid from managed EV load paired with high renewables seems substantial, we estimate that the installed cost of an EVSE must be between $1000 and $3000 for a ten-year life to be cost neutral, depending on grid conditions. [ABSTRACT FROM AUTHOR]

Spinning reserve (SR) is one of the most important resources used by system operators to respond to unforeseen events. In a low-carbon economy mode, carbon capture plant is an important CO2 abatement option and might constitute a significant proportion of the generation mix. Due to their potential flexibility, such plants might affect the requirement for SR. This paper describes a multi-period optimization model for coordinating the generation, carbon capture and SR provision. In turn, this model is used to optimize the SR requirements, with the objective of minimizing the overall cost. Since this objective function combines the generation production cost, the expected energy not supplied (EENS) cost, SR provision cost and the carbon emission cost, it is better suited to evaluate the SR requirements of power system with carbon capture plants. Numerical results based on a 26-unit test system show that the proposed model is effective and practical. [ABSTRACT FROM PUBLISHER]

In order to achieve sustainable development goals, modern power systems need to achieve a low-carbon transition. Retirement and replacement of aging coal-fired power plants (CFPP) is the major part of electricity network transition. However, the sudden retirement of large aging CFPP could lead to power supply or reserve shortages and generally, it takes several years to complete the replacement process. This paper proposes a low-carbon electricity network transition model, helping to plan aging CFPP retirement and renewable power plants installation as well as network augmentation. The trade-off decisions are found among three conflicting objectives including cost, risk and carbon emission. Moreover, the carbon emission flow (CEF) model is applied in the planning process to calculate carbon emissions from the demand side, in order to assess the efficacy of the low-carbon transition. A relatively new multi-objective natural aggregation algorithm (MONAA) is introduced and applied to find the optimal solution. The proposed model is verified on a modified IEEE 24-bus RTS system and a modified IEEE 118-bus system. According to the numerical results, the proposed model can achieve carbon mitigation, cost and risk benefits and provide a roadmap to guide the energy network transformation towards a low-carbon economy. [ABSTRACT FROM AUTHOR]

The rapid increase in wind power capacity over the past decade has resulted from the adoption of policies that encourage the wider use of renewable energy sources in order to reduce CO2 and the dramatic cost reductions due to technology advancements. The high variability in wind speeds poses major difficulties in power system planning and operations, leading to an acute need for practical planning and operations tools to study the effects of the integration of wind resources into the grid. This paper addresses the need in the planning domain through the development of a computationally efficient probabilistic production simulation approach with the capability to quantify the variable effects of systems for varying levels of wind penetration with the uncertainty in the variability/intermittency effects of wind generation at multiple sites together with the other sources of uncertainty explicitly represented. The simulation approach is based on the identification of the prevailing wind regimes in the regions where wind resources are located and the judicious application of conditional probability concepts in incorporating the wind regime representation. The regimes-based approach described in the paper effectively captures both the seasonal and the diurnal variations of renewable resources and their correlation with the load seasonal and diurnal changes. Additionally, the proposed approach explicitly quantifies the impacts of wind on the additional reserve requirements on the controllable resources. The paper illustrates the effectiveness of the approach by its application to large-scale test systems using historical data. [ABSTRACT FROM PUBLISHER]

As a clean fuel source, natural gas plays an important role in achieving a low-carbon economy in the power industry. Owing to the uncertainties introduced by increasing utilization of natural gas in electric power system, gas system and electricity system should be planned in an integrated manner. When considering these two systems simultaneously, there are many emerging difficulties, e.g., increased system complexity and risk, market timeline mismatch, overall system reliability evaluation, etc. In this paper, a novel expansion co-planning (ECP) framework is proposed to address the above challenges. In our approach, the planning process is modeled as a mixed integer nonlinear optimization problem. The best augmentation option is a plan with the highest cost/benefit ratio. Benefits of expansion planning considered are reductions in operation cost, carbon emission cost, and unreliability cost. By identifying several scenarios based on statistical analysis and expert knowledge, decision analysis is used to tackle market uncertainties. The operational and economic interdependency of both systems are well analyzed. Case studies on a three-bus gas and two-bus power system, plus the Victorian integrated gas and electricity system in Australia are presented to validate the performance of the proposed framework. [ABSTRACT FROM PUBLISHER]

The most dangerous effects of anthropogenic climate change can be mitigated by using emissions taxes or other regulatory interventions to reduce greenhouse gas (GHG) emissions. This paper takes a regulatory viewpoint and describes the Weighted Sum Bisection method to determine the lowest emission tax rate that can reduce the anticipated emissions of the power sector below a prescribed, regulatorily-defined target. This bilevel method accounts for a variety of operating conditions via stochastic programming and remains computationally tractable for realistically large planning test systems, even when binary commitment decisions and multiperiod constraints on conventional generators are considered. Case studies on a modified ISO New England test system demonstrate that this method reliably finds the minimum tax rate that meets emissions targets. In addition, it investigates the relationship between system investments and the tax-setting process. Introducing GHG emissions taxes increases the value proposition for investment in new cleaner generation, transmission, and energy efficiency; conversely, investing in these technologies reduces the tax rate required to reach a given emissions target. [ABSTRACT FROM AUTHOR]

In a carbon-constrained world, CO2 emissions will become a new concern in power system dispatch. Meanwhile, carbon capture power plants (CCPPs), which are a critical low-carbon power generation option, will have a significant impact on power system operation and dispatch. This paper presents research on low-carbon power system dispatch (LCPSD) incorporating CCPPs. The operating mechanism of CCPPs is investigated first. Then, the operating characteristics of CCPPs in power system dispatch are analyzed, including feasible power output limits, ramping rates and relationships between the power outputs and the carbon emissions. A comprehensive LCPSD model is formulated, in which the carbon emissions of power plants are treated as a new set of decision variables, and low-carbon-related cost terms are considered. The dispatch features of CCPPs are elaborately formulated and incorporated into the LCPSD model. The effectiveness and the validity of the proposed LCPSD mode and model are demonstrated using numerical examples based on an IEEE 118-bus tested system. [ABSTRACT FROM PUBLISHER]

In deregulated electricity sector climates, such as in Ontario, the production of clean or renewable energy by small power producers through distributed generation (DG) is encouraged. This paper examines the policies that can be used to encourage DG investment and incorporates them into a mathematical model. This model is then used to create scenarios for examining the economic and environmental supply-side effects of policies to a distribution system over a ten-year period. The policies analyzed include a combination of feed-in-tariffs, CO2 tax, and cap-and-trade schemes. The results are discussed in the context of the Ontario market and its Standard Offer Program, implemented on a 32-bus radial distribution system. [ABSTRACT FROM AUTHOR]

This paper proposes a plant growth-based optimization approach for capacitor placement in power systems. The method characterizes the growth mechanism of a plant phototropism, where a larger morphactin concentration of a node indicates a higher probability of new branch growth. The growth of a plant phototropism is mimicked in a function optimization process that is useful for capacitor placement. Through this capacitor allocation, the line loss and carbon dioxide emissions of the grid can be both reduced. The proposed method is validated using example grids and a part of the Taipower Company system. Test results confirm the feasibility of the method. [ABSTRACT FROM PUBLISHER]

We present a new formulation of a hybrid stochastic-robust optimization and use it to calculate a look-ahead, security-constrained optimal power flow. It is designed to reduce carbon dioxide (CO2) emissions by efficiently accommodating renewable energy sources and by realistically evaluating system changes that could reduce emissions. It takes into account ramping costs, CO2 damages, demand functions, reserve needs, contingencies, and the temporally linked probability distributions of stochastic variables such as wind generation. The inter-temporal trade-offs and transversality of energy storage systems are a focus of our formulation. We use it as part of a new method to comprehensively estimate the operational net benefits of system changes. Aside from the optimization formulation, our method has four other innovations. First, it statistically estimates the cost and CO2 impacts of each generator's electricity output and ramping decisions. Second, it produces a comprehensive measure of net operating benefit, and disaggregates that into the effects on consumers, producers, system operators, government, and CO2 damage. Third and fourth, our method includes creating a novel, modified Ward reduction of the grid and a thorough generator dataset from publicly available information sources. We then apply this method to estimating the impacts of wind power, energy storage, and operational policies. [ABSTRACT FROM PUBLISHER]

In the electricity sector the carbon tax is a common environmental policy aiming to reduce CO2 emissions, but is often regarded as economically unfriendly, especially for areas relying on coal-fire and other carbon-intensive generators. A power grid utilizing an energy storage system can be a promising solution to alleviate the regional economy pressure in a grid where the carbon tax is enforced. With the increasing exploitation of clean energy, e.g., solar and wind power, in this work, we characterize the stochastic emission-aware economic dispatch with a storage system utilizing two frameworks, namely a chance-constrained framework and a robust optimization framework. We highlight their differences and connections by studying the trade-offs between robustness and overall cost. Specifically, we bridge the two frameworks with a novel distributed robust optimization framework that considers practical bounds to estimate the optimal system performance under the reliability requirement. Numerical studies on the six-bus model and the IEEE-118 bus model further justify our findings. [ABSTRACT FROM AUTHOR]

We study the equilibria reached in electricity, natural-gas, and carbon-emission markets, where power producers simultaneously participate in these three markets. We use a conjectural-variations equilibrium model to represent both electricity and natural-gas markets, in which power/natural gas-producers play a quantity game and adjust their production decisions in reaction to the production levels of their rivals. We use a cap-and-trade mechanism to model the carbon-emission market. We develop a direct approach to identify equilibria involving the three markets, in which the optimality conditions of all players and market clearing conditions are gathered and solved jointly. Numerical results from two test systems illustrate the impact of carbon-emission trading and transmission constraints on the equilibria reached. Additionally, we compare equilibrium results under different types of competition. [ABSTRACT FROM AUTHOR]

While energy arbitrage from energy storage can lower power system operating costs, it can also increase greenhouse gas emissions. If power system operations are conducted with the constraint that energy storage operation must not increase emissions, how does this constraint affect energy storage investment decisions? Two bi-level energy storage investment problems are considered, representing ‘philanthropic’ (profitability-constrained) and profit-maximizing storage investors (PhSI, PMSI). A MILP heuristic is developed to obtain good candidate solutions to these inherently MINLP bi-level problems. A case study is conducted on a 30% renewable system, with sensitivity analyses on the price of storage and the price of carbon emissions. Regardless of the emissions-neutrality constraint, a PhSI installs significantly more energy storage than a PMSI, increasing system flexibility. The effect of the emissions-neutrality constraint in the absence of a carbon price is to reduce the quantity of storage purchased and reduce annual system emissions (${\scriptstyle\sim}$ 3%), with only minor increases in overall cost (${\scriptstyle\sim}$ 0.1%). In cases with a carbon price, storage does not tend to increase emissions and the emissions constraint does not tend to decrease storage investment. The emissions-neutrality constraint is seen to deliver similar emissions reductions even in a system with much higher renewable penetration (46%). [ABSTRACT FROM AUTHOR]

We consider a mid-sized private electricity generating company that plans to enter the market that is partially regulated. There is a cap and trade system in operation in the industry. There are nine possible power plant types that the company considers to invest on through a planning horizon. Some of these plants may include a carbon capture and storage technology. We develop a stochastic mixed-integer linear program for this problem where the objective is to maximize the expected net present value of the profit obtained. We include restrictions on the maximum and minimum possible amount of investment for every type of investment option. We also enforce market share conditions such that the percentage of the total investments of the company over the total installed capacity of the country stay between upper and lower bounds. Moreover, in order to distribute the investment risk, the percentage of each type of power plant investment is restricted by some upper bound. We tested the model for a hypothetical company operating in Turkey. The results show that the model is suitable to be used for determining the investment strategy of the company. [ABSTRACT FROM PUBLISHER]

The objective of this work is to present a methodology capable of estimating the incremental payoff of the carbon market for grid-connected renewable power generation projects in Brazil. The proposed methodology consists of using the NEWAVE model to determine the optimal operating policy for the Brazilian Interconnected System over the medium-term, and to establish hydrological dispatch scenarios for the power plants connected to this system. Based on these results, the criteria defined in the methodology ACM0002 were used to estimate the baseline of the projects developed in response to the Clean Development Mechanism of the Kyoto Protocol. Having estimated the baseline for the project, the incremental payoff obtained from the sale of Certified Emission Reductions is estimated using the real options theory. Subsequently, the value of the option is added to the conventional cash flow of the project, determining the impact of the carbon market on its internal rate of return. [ABSTRACT FROM PUBLISHER]

A general model of long-term equilibrium in energy only electricity markets that allows for generation additions and retirements is developed. The model is then applied with different assumptions regarding generator behaviors. The distribution of electricity prices, reliability of supply, and overall CO_2 emissions at equilibrium are analytically found for fully competitive markets with and without inflexible generation. A method to determine long-term equilibrium in markets with a mix of competitive and inflexible generators and a strategic supplier is proposed and illustrated. [ABSTRACT FROM PUBLISHER]