Consideration of system security criteria in the models of power system unit commitment

The main objective of the electric power industry is to provide for uninterrupted and reliable operation of power systems in order to meet the demand for electric power. The introduction of digital technologies in the wholesale electricity and capacity market is aimed at improving the efficiency, safety and continuity of its operation. The author highlights the features of legal support of digitalization in the wholesale electricity and capacity market, and proposes the possibility of expansion of the experimental legal regime to the wholesale electricity and capacity market. Information security issues are considered taking into account the fact that market participants may suffer not only financial and reputational losses from the implementation of security threats, but also damage to the life and health of people, the environment and the market infrastructure. The author concludes that it is necessary to develop means of legal protection of interests of owners and users of electronic software systems, as well as legal regulation of automated actions carried out in the wholesale electricity and capacity market. The research was carried out with the financial support of the RFBR within scientific project No. 20-311-90034.

The main objective of the electric power industry is to provide for uninterrupted and reliable operation of power systems in order to meet the demand for electric power. The introduction of digital technologies in the wholesale electricity and capacity market is aimed at improving the efficiency, safety and continuity of its operation. The author highlights the features of legal support of digitalization in the wholesale electricity and capacity market, and proposes the possibility of expansion of the experimental legal regime to the wholesale electricity and capacity market. Information security issues are considered taking into account the fact that market participants may suffer not only financial and reputational losses from the implementation of security threats, but also damage to the life and health of people, the environment and the market infrastructure. The author concludes that it is necessary to develop means of legal protection of interests of owners and users of electronic software systems, as well as legal regulation of automated actions carried out in the wholesale electricity and capacity market. The research was carried out with the financial support of the RFBR within scientific project No. 20-311-90034.

Two goods are traded on the modern wholesale electricity and capacity market in Russia: electricity and capacity. More than 80% of the electricity market consists of free (unregulated) sales, the price of which is not determined by the decision of the regulator, but as a result of the operation of a certain market mechanism. Under these conditions, the ability to predict the price of electricity becomes significant for market participants, as evidenced by the presence of publications that develop this topic. In order to build this forecast, it is useful to take into account the opinion of other market participants about the future behavior of the price of electricity, to be able to understand and interpret their forecast and analytical models.

The article aims to analyze the pricing processes in the wholesale electricity and capacity market of the Russian Federation and to identify their problems and peculiar features. The tested hypothesis of the study is that the peculiar features of pricing in united energy systems are explained by the influence of several separate but interrelated factors that cause multidirectional problems when generating and grid companies implement pricing strategies under the conditions of energy transition, which implies refocusing on renewable energy sources. The content-related methodology of the article includes the study of electric power and capacity pricing regularities and is based on the system approach. The research tools included analysis and synthesis, study and generalization, statistical and economic analysis, and graphical method. The adequate objectives of the article are represented by the scientific analysis of modern views of re-searchers on the problems and peculiar features of pricing based on the results of the evolution of publica-tions in this field; analysis of the current state of the energy industry in the Russian Federation; determining the volume of electricity consumption and actual generation in the Russian Federation; analysis of the dis-tribution of investment projects in the energy industry according to the structure of united Russian energy systems, and analysis of the problems and peculiar features of pricing in the territories of the Central IPS (integrated power system), the North-West IPS, the South IPS, the Middle Volga IPS, the Urals IPS, the Si-beria IPS, the East IPS. The results obtained according to the verification of the research hypothesis allowed identifying separate but interrelated factors which cause differently directed problems in the implementation of pricing strategies by generating and grid companies and affect the specifics of pricing: concentration of large industrial consumers; infrastructural development of energy facilities and the extensive power trans-mission network; competition between players in the wholesale electricity and capacity market; the share of regulated tariffs in the wholesale electricity and capacity market; development of investment raising in the electric power industry; compensation of costs incurred by generating and grid companies through state subsidies; introduction of technologies aimed at improving pricing management efficiency in electricity consumption demand management; nodal price management to ensure market equilibrium in pricing; price index management under various operating scenarios of the energy system. Brief results of the work reflect-ing the practical significance of the research findings are determined by their applicability in the activities of generating and grid companies of the RF energy industry aimed at improving pricing management effi-ciency under the conditions of energy transition and raising energy efficiency.

This paper presents the solving unit commitment (UC) problem using Modified Subgradient Method (MSG) method combined with Simulated Annealing (SA) algorithm. UC problem is one of the important power system engineering hard-solving problems. The Lagrangian relaxation (LR) based methods are commonly used to solve the UC problem. The main disadvantage of this group of methods is the difference between the dual and the primal solution which gives some significant problems on the quality of the feasible solution. In this paper, MSG method which does not require any convexity and differentiability assumptions is used for solving the UC problem. MSG method depending on the initial value reaches zero duality gap. SA algorithm is used in order to assign the appropriate initial value for MSG method. The major advantage of the proposed approach is that it guarantees the zero duality gap independently from the size of the problem. In order to show the advantages of this proposed approach, the four-unit Tuncbilek thermal plant and ten-unit thermal plant which is usually used in literature are chosen as test systems. Penalty function (PF) method is also used to compare with our proposed method in terms of total cost and UC schedule.

Alberta’s electricity market is currently undergoing a period of substantial transition. The province should proceed with caution as it switches from an energy-only electricity market to a capacity market by 2021. Many other jurisdictions have already made the changeover and Alberta can learn from their experiences in order to avoid common mistakes and pitfalls that can arise with the deployment of a capacity market. There were growing concerns that the existing electricity market structure would not attract sufficient investment from conventional generation (e.g., natural gas) due to the increased penetration of zero marginal cost renewable generation. As a result, the Alberta government has chosen to transition to a capacity market. For consumers, a capacity market aims to ensure there is sufficient investment in new generation capacity to “keep the lights on” and reduce price swings in the wholesale market. The capacity market will also help the province meet its goals for attracting investors and transitioning away from its dependence on coal-fired electricity generation. However, a switchover is not as simple as it sounds. In an energy-only market, firms are paid solely based on the provision of electricity in hourly wholesale markets. In capacity markets, electricity-generating firms are also paid for providing generation capacity, reflecting the potential to provide electricity at some point in the future. While capacity markets can help ensure there is a reliable supply of electricity, there are several challenges in the implementation of capacity markets. This paper discusses the motivation for the adoption of capacity markets, highlights challenges regulators face when implementing this market design in the context of Alberta, and summarizes the key trade-offs associated with energy-only versus capacity market designs. Relative to an energy-only market, a capacity market is more complex and requires that regulators specify numerous parameters that are essential to the functioning of the market. An essential, but often controversial component is the formulation of the capacity demand curve. A capacity demand curve for Alberta has to be carefully designed to deal with uncertainties in demand growth, given that Alberta’s electricity demand is closely interconnected with the ups and downs of global crude oil prices. Consideration must be given to the perspective of outside investors who – as in any area of economic interest – are wary about uncertainty. Political and regulatory uncertainty can undermine the success of a capacity market. This potential for investor hesitancy could result in incumbent firms, familiar with investing in Alberta, seizing a larger share of the market in an already historically concentrated environment. It is critical that policymakers establish a clear and well-defined trajectory for the future of Alberta’s electricity market design as a whole, not just its capacity market. The capacity market is not a panacea for the potential downfalls of an energy-only market. There are trade-offs associated with both energy-only and capacity market designs. Energy-only markets are arguably more economically efficient with cleaner price signals. However, with political constraints on electricity price-spikes and the expansion of renewables, there is more uncertainty in an energy-only market’s ability to promote investment. A capacity market provides more certainty in terms of generation resource adequacy, but at a potentially higher cost. Despite these tradeoffs, capacity markets are unambiguously more complex. This places a heavy burden on regulators to carefully and correctly set critical capacity market parameters that can have substantive impacts on prices and the associated economic signals.

Study on unit commitment problem considering pumped storage and renewable energy via a novel binary artificial sheep algorithm

The unit commitment operation in power systems is a complex, non-linear, constrained optimization problem. The commitment and de-commitment decision presents a binary problem which needs discrete/binary optimization approaches. This paper presents a binary bat search algorithm (BBSA) to solve unit commitment (UC) problem. The bat search algorithm belongs to a meta heuristic class of optimization approaches inspired by natural echolocation behavior of bats. In order to solve binary UC problem, the real valued bat search process is mapped to binary search space using sigmoidal transformation function. The BBSA is then applied to test system with 10 thermal units. The effectiveness of the BBSA is verified against system dimension using test systems upto 100 units. Extensive numerical experiments are performed to test the effectiveness of BBSA and statistical analysis of simulation results are presented. The simulation results are presented, discussed and compared to various existing classical and heuristic approaches. The same demonstrate the superior performance of BBSA approach in solving UC problem.

<p>This paper proposed the integration of solar energy resources into the conventional unit commitment. The growing concern about the depletion of fossil fuels increased the awareness on the importance of renewable energy resources, as an alternative energy resources in unit commitment operation. However, the present renewable energy resources is intermitted due to unpredicted photovoltaic output. Therefore, Ant Lion Optimizer (ALO) is proposed to solve unit commitment problem in smart grid system with consideration of uncertainties .ALO is inspired by the hunting appliance of ant lions in natural surroundings. A 10-unit system with the constraints, such as power balance, spinning reserve, generation limit, minimum up and down time constraints are considered to prove the effectiveness of the proposed method. The performance of proposed algorithm are compared with the performance of Dynamic Programming (DP). The results show that the integration of solar energy resources in unit<em> commitment scheduling can improve the total operating cost significantly. </em></p>

Developing a legal framework for energy storage technologies in the U.S: The case of pumped underground storage hydro

A two-stage stochastic programming scheme is proposed in order to evaluate the offering strategy of a distributed energy resource aggregator in both wholesale and local electricity markets and appropriately cope with uncertainties associated with its decision-making problem. In this regard, the aggregator combines a broad range of virtual and real distributed energy resources to simultaneously participate in the local electricity market as a price-maker or strategic player and the wholesale electricity market as a price-taker or non-strategic player. To model the studied aggregator as a strategic entity in the local market, a bilevel programming approach is exploited in this work. Accordingly, at the upper level of the raised problem, the aggregator tends to promote its expected profit through taking part in the wholesale and local electricity markets, while at the lower level, the considered local market is cleared in a way to maximise the social welfare. In the end, the effectiveness of the proposed framework for the simultaneous participation of the distributed energy resource aggregator in these two markets has been explored utilising a case study.

Solving the unit commitment (UC) problem in a computationally efficient manner is a critical issue of electricity market operations. Optimization-based methods such as heuristics, dynamic programming, and mixed-integer quadratic programming (MIQP) often yield good solutions to the UC problem. However, the computation time of optimization-based methods grows exponentially with the number of generating units, which is a major bottleneck in practice. To address this issue, we formulate the UC problem as a Markov decision process and propose a novel multi-step deep reinforcement learning (RL)-based algorithm to solve the problem. We approximate the action-value function with neural networks and design an algorithm to determine the feasible action space. Numerical studies on a 5-generator test case show that our proposed algorithm significantly outperforms the deep Q-learning and yields similar level of performance as that of MIQP-based optimization in terms of optimality. The computation time of our proposed algorithm is much shorter than that of MIQP-based optimization methods.

A mixed integer linear programming (MILP) approach for the joint simulation of electric control reserve and electricity wholesale markets is presented. This generation dispatch model extends an existing integrated grid and electricity market (IGEM) model covering the Continental European electric power system. By explicitly incorporating the markets for primary and secondary control reserves (PCR and SCR), the model can reproduce the decisions of generating unit operators on which markets get involved. Besides, the introduction of the integrality conditions allows considering start-up costs and the calculus of generating units to pass through the economically unattractive periods with low or even negative prices in order to avoid another start-up. Since this model is too large to be solved with common MILP solvers for the intended simulation time of one year, temporal and geographical interdependencies are used to solve it heuristically. The heuristic therefore splits the model into various sub-problems so that on the one hand, the number of variables, especially of integer variables, per sub-problem is reduced significantly and on the other hand, the relevant interdependencies remain considered. The heuristic is evaluated in terms of accuracy and computation time by means of two case studies. Both case studies show satisfactory accuracy and significant advantages in computation time.

While many European countries are implementing different capacity remuneration mechanisms (CRMs) or capacity markets, the Russian electricity industry faces the consequences of the capacity market with CRMs, i.e. capacity oversupply. Russia introduced the capacity market and CRMs for new capacity construction in 2011. Ideally, old inefficient generation should have exited the market leaving new and efficient ones. However, high number of “must-run” power plants that cannot be excluded in the capacity auction and overestimated capacity demand have led to an oversupply of capacity. In this paper we estimate the capacity market clearing price based on sloping demand and supply functions as well as test the possibility of new efficient generation entry. The outcomes of the model are used to predict the consumer capacity price peak due to CRMs and to conclude on efficiency of implementing sloping capacity demand curve in capacity auctions.

The interaction of wholesale electricity market structures under futures with decarbonization policy goals: A complexity conundrum